Author: "Fabien Lotte" / Topic: brain-computer interface - Searchworks@Jio Institute Digital Library Search Results

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1. Editorial: Long Term User Training and Preparation to Succeed in a Closed-Loop BCI Competition

Author: Gernot R. Müller-Putz, Damien Coyle, Fabien Lotte, Jing Jin, and David Steyrl
Subjects: Brain-Computer Interface, electroencephalogram, transfer learning, user learning, user-centered training, closed-loop learning, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Published: 2022
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2. BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification

Author: Fabien Lotte, Aurélien Appriou, Dan Dutartre, Léa Pillette, Andrzej Cichocki, David Trocellier, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria), Skolkovo Institute of Science and Technology [Moscow] (Skoltech), and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest
Subjects: OpenVibe, Computer science, 0206 medical engineering, TP1-1185, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Electroencephalography, Machine learning, computer.software_genre, Biochemistry, Python platform, Article, physiological signals, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Analytical Chemistry, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, brain–computer interfaces (BCI), medicine, Animals, Biosignal, electroencephalography (EEG), [INFO.INFO-BT]Computer Science [cs]/Biotechnology, Electrical and Electronic Engineering, signal processing, Instrumentation, Protocol (object-oriented programming), Graphical user interface, computer.programming_language, Brain–computer interface, Signal processing, medicine.diagnostic_test, business.industry, Chemical technology, Brain, Signal Processing, Computer-Assisted, Python (programming language), 020601 biomedical engineering, Atomic and Molecular Physics, and Optics, Boidae, machine learning, Brain-Computer Interfaces, Artificial intelligence, business, computer, Algorithms, 030217 neurology & neurosurgery
Abstract: International audience; Research on brain–computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithmsbefore using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals.
Published: 2021
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3. Guidelines to use Transfer Learning for Motor Imagery Detection: an experimental study

Author: Fabien Lotte, Laurent Bougrain, Pedro Rodrigues, Geoffrey Canron, Sébastien Rimbert, Analysis and modeling of neural systems by a system neuroscience approach (NEUROSYS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Psychologie Ergonomique et Sociale pour l'Expérience utilisateurs (PErSEUs), Université de Lorraine (UL), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, The author acknowledges the support of the French Agence Nationale de la Recherche (ANR) under reference ANR-19-CE33-0007 (Grasp-IT project), and the European Research Council with project BrainConquest (grant ERC-2016-STG-714567)., European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Bougrain, Laurent, and Boosting Brain-Computer Communication with high Quality User Training - BrainConquest - - H2020 Pilier ERC2017-07-01 - 2022-06-30 - 714567 - VALID
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, 0206 medical engineering, 02 engineering and technology, Machine learning, computer.software_genre, Session (web analytics), Domain (software engineering), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, [SCCO]Cognitive science, 0302 clinical medicine, Motor imagery, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Set (psychology), Reliability (statistics), Brain–computer interface, business.industry, Neural engineering, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [SCCO] Cognitive science, 020601 biomedical engineering, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Artificial intelligence, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], Transfer of learning, business, computer, 030217 neurology & neurosurgery
Abstract: International audience; Brain-Computer Interfaces (BCI) based on Motor imagery (MI) shown promising results for motor recovery, intraoperative awareness detection or assistive technology control. However, they suffer from several limitations due to the high variability of electroencephalographic (EEG) signals, mainly lengthy and tedious calibration times usually required for each new day of use, and a lack of reliability for all users. Such problems can be addressed, to some extent, using transfer learning algorithms. However, the performance of such algorithms has been very variable so far, and when they can be safely used is still unclear. Therefore, in this article, we study the performance of various state-of-the-art Riemannian transfer learning algorithms on a MI-BCI database (30 users), for various conditions: 1) supervised and unsupervised transfer learning; 2) for various amount of available training EEG data for the target domain; 3) intra-session or inter-session transfer; 4) for both users with good and less good MI-BCI performances. From such experiments, we derive guidelines about when to use which algorithm. Re-centering the target data is effective as soon as a few samples of this target set are taken into account. This is true even for an intra-session transfer learning. Likewise, re-centering is particularly useful for subjects who have difficulty producing stable motor imagery from session to session.
Published: 2021

4. Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training

Author: Fabien Lotte, Bertrand Glize, Bernard N'Kaoua, Romain Sabau, Léa Pillette, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École Centrale de Nantes (ECN), Handicap Activité Cognition Santé [Bordeaux] (HACS), Université de Bordeaux (UB)-Institut National de Recherche en Informatique et en Automatique (Inria)-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU de Bordeaux Pellegrin [Bordeaux], This research was funded by the French National Research Agency (project REBEL, grant ANR-15-CE23-0013-01) and the European Research Council with the Brain-Conquest project (grant ERC-2016-STG-714567)., ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), Lotte, Fabien, Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle - - REBEL2015 - ANR-15-CE23-0013 - AAPG2015 - VALID, Boosting Brain-Computer Communication with high Quality User Training - BrainConquest - - H2020 Pilier ERC2017-07-01 - 2022-06-30 - 714567 - VALID, and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest
Subjects: Neuroprosthetics, Computer Networks and Communications, Computer science, Brain activity and meditation, InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI), media_common.quotation_subject, Control (management), Neuroscience (miscellaneous), lcsh:Technology, 050105 experimental psychology, Session (web analytics), realistic visual feedback, 03 medical and health sciences, Presentation, [SCCO]Cognitive science, 0302 clinical medicine, Human–computer interaction, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], lcsh:Science, user training, Brain–computer interface, media_common, Modalities, Modality (human–computer interaction), lcsh:T, 05 social sciences, [SCCO] Cognitive science, multimodal feedback, Computer Science Applications, Human-Computer Interaction, vibrotactile feedback, motor imagery based brain-computer interfaces, lcsh:Q, Brain-computer interfaces, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], 030217 neurology & neurosurgery
Abstract: International audience; By performing motor-imagery tasks, for example, imagining hand movements, Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) users can control digital technologies, for example, neuroprosthesis, using their brain activity only. MI-BCI users need to train, usually using a unimodal visual feedback, to produce brain activity patterns that are recognizable by the system. The literature indicates that multimodal vibrotactile and visual feedback is more effective than unimodal visual feedback, at least for short term training. However, the multi-session influence of such multimodal feedback on MI-BCI user training remained unknown, so did the influence of the order of presentation of the feedback modalities. In our experiment, 16 participants trained to control a MI-BCI during five sessions with a realistic visual feedback and five others with both a realistic visual feedback and a vibrotactile one. training benefits from a multimodal feedback, in terms of performances and self-reported mindfulness. There is also a significant influence of the order presentation of the modality. Participants who started training with a visual feedback had higher performances than those who started training with a multimodal feedback. We recommend taking into account the order of presentation for future experiments assessing the influence of several modalities of feedback.
Published: 2021
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5. Experimenters Influence on Mental-Imagery based Brain-Computer Interface User Training

Author: Léa Pillette, Fabien Lotte, Aline Roc, Bernard N'Kaoua, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Popular interaction with 3d content (Potioc), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Handicap Activité Cognition Santé [Bordeaux] (HACS), Université de Bordeaux (UB)-Institut National de Recherche en Informatique et en Automatique (Inria)-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM), ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Pillette, Léa, Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle - - REBEL2015 - ANR-15-CE23-0013 - AAPG2015 - VALID, and Boosting Brain-Computer Communication with high Quality User Training - BrainConquest - - H2020 Pilier ERC2017-07-01 - 2022-06-30 - 714567 - VALID
Subjects: Social psychology (sociology), Experimenter influence, Brain activity and meditation, Control (management), Human Factors and Ergonomics, Context (language use), Mental imagery, 050105 experimental psychology, Session (web analytics), Education, 03 medical and health sciences, [SCCO]Cognitive science, 0302 clinical medicine, Motor imagery, [INFO.EIAH] Computer Science [cs]/Technology for Human Learning, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Brain–computer interface, User training, 05 social sciences, General Engineering, Gender, [SCCO] Cognitive science, Human-Computer Interaction, Hardware and Architecture, Brain-Computer Interfaces, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], Psychology, 030217 neurology & neurosurgery, Software, Cognitive psychology, Mental image
Abstract: International audience; Context Motor Imagery based Brain-Computer Interfaces (MI-BCIs) enable their users to interact with digital technologies, e.g., neuroprosthesis, by performing motor imagery tasks only, e.g., imagining hand movements, while their brain activity is recorded. To control MI-BCIs, users must train to control their brain activity. During such training, experimenters have a fundamental role, e.g., they motivate participants. However, their influence had never been formally assessed for MI-BCI user training. In other fields, e.g., social psychology, experimenters’ gender was found to influence experimental outcomes, e.g., behavioural or neurophysiological measures.ObjectiveOur aim was to evaluate if the experimenters’ gender influenced MI-BCI user training outcomes, i.e., performances and user-experience.MethodsWe performed an experiment involving 6 experimenters (3 women) each training 5 women and 5 men (60 participants) to perform right versus left hand MI-BCI tasks over one session. We then studied the training outcomes, i.e., MI-BCI performances and user-experience, according to the experimenters' and subjects' gender.ResultsA significant interaction between experimenters’ and participants' gender was found on the evolution of trial-wise performances. Another interaction was found between participants’ tension and experimenters’ gender on the average performances.ConclusionExperimenters’ gender could influence MI-BCI performances depending on participants’ gender and tension.SignificanceExperimenters’ influence on MI-BCI user training outcomes should be better controlled, assessed and reported to further benefit from it while preventing any bias.
Published: 2021

6. Grand Challenges in Neurotechnology and System Neuroergonomics

Author: Stephen H. Fairclough, Fabien Lotte, Liverpool John Moore University (ljmu), Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), FL was partly supported by funding from the European Research Council with project BrainConquest (grant ERC-2016-STG-714567)., and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Subjects: Computer science, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, BF, ComputerApplications_COMPUTERSINOTHERSYSTEMS, fNIRS, Neurotechnologies, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, InformationSystems_GENERAL, [SCCO]Cognitive science, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Neurotechnology, Neuroergonomics, ComputingMilieux_COMPUTERSANDEDUCATION, 0501 psychology and cognitive sciences, EEG, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-BT]Computer Science [cs]/Biotechnology, 050107 human factors, ComputingMilieux_MISCELLANEOUS, Brain–computer interface, Grand Challenges, Cognitive science, ComputingMilieux_THECOMPUTINGPROFESSION, [SCCO.NEUR]Cognitive science/Neuroscience, 05 social sciences, Human-computer interaction, ComputingMilieux_GENERAL, [SCCO.PSYC]Cognitive science/Psychology, RC0321, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], Brain-computer interfaces, 030217 neurology & neurosurgery
Abstract: SPECIALTY GRAND CHALLENGE ARTICLE
Published: 2020

7. Channel selection over riemannian manifold with non-stationarity consideration for brain-computer interface applications

Author: Léa Pillette, Thibaut Monseigne, Khadijeh Sadatnejad, Fabien Lotte, Aline Roc, Aurélien Appriou, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Sadatnejad, Khadijeh, and Boosting Brain-Computer Communication with high Quality User Training - BrainConquest - - H2020 Pilier ERC2017-07-01 - 2022-06-30 - 714567 - VALID
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Covariance matrix, 0206 medical engineering, 02 engineering and technology, law.invention, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Discriminative model, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, law, 0202 electrical engineering, electronic engineering, information engineering, EEG, BCI, Brain–computer interface, Riemannian manifold, business.industry, 020206 networking & telecommunications, Pattern recognition, 020601 biomedical engineering, Manifold, Outlier, Artificial intelligence, Brain-computer interfaces, business, Manifold (fluid mechanics), Channel selection, Communication channel
Abstract: International audience; In this paper, we propose and compare multiple criteria for selecting ElectroEncephaloGraphic (EEG) channels over the Riemannian manifold, for EEG classification in Brain-Computer Interfaces (BCI). These criteria aim to promote EEG covariance matrix classifiers to generalize well by considering EEG data non-stationarity. Our approach consists of both increasing the discriminative information between classes over the manifold and reducing the dispersion within classes. We also reduce the influence of outliers in both discriminative and dispersion measures. Using the proposed criteria, channel selection is done automatically in a backward elimination process. The criteria are evaluated on EEG signals recorded from a tetraplegic subject and dataset IVa from BCI competition III. Experimental evidences confirm that considering the dispersion within each class as a measure for quantifying the effects of non-stationarity and removing the most affected channels can improve the performance of BCI by 5% on the tetraplegic subject and by 12 % on dataset IVa.
Published: 2020

8. Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems

Author: Sebastián Castaño-Candamil, Michael Tangermann, Fabien Lotte, Benjamin Blankertz, and Andreas Meinel
Subjects: Hyperparameter, Computer science, Covariance matrix, business.industry, General Neuroscience, Model selection, 05 social sciences, Pattern recognition, Overfitting, Regularization (mathematics), 050105 experimental psychology, Tikhonov regularization, 03 medical and health sciences, 0302 clinical medicine, 0501 psychology and cognitive sciences, Artificial intelligence, business, 030217 neurology & neurosurgery, Software, Decoding methods, Information Systems, Brain–computer interface
Abstract: We report on novel supervised algorithms for single-trial brain state decoding. Their reliability and robustness are essential to efficiently perform neurotechnological applications in closed-loop. When brain activity is assessed by multichannel recordings, spatial filters computed by the source power comodulation (SPoC) algorithm allow identifying oscillatory subspaces. They regress to a known continuous trial-wise variable reflecting, e.g. stimulus characteristics, cognitive processing or behavior. In small dataset scenarios, this supervised method tends to overfit to its training data as the involved recordings via electroencephalogram (EEG), magnetoencephalogram or local field potentials generally provide a low signal-to-noise ratio. To improve upon this, we propose and characterize three types of regularization techniques for SPoC: approaches using Tikhonov regularization (which requires model selection via cross-validation), combinations of Tikhonov regularization and covariance matrix normalization as well as strategies exploiting analytical covariance matrix shrinkage. All proposed techniques were evaluated both in a novel simulation framework and on real-world data. Based on the simulation findings, we saw our expectations fulfilled, that SPoC regularization generally reveals the largest benefit for small training sets and under severe label noise conditions. Relevant for practitioners, we derived operating ranges of regularization hyperparameters for cross-validation based approaches and offer open source code. Evaluating all methods additionally on real-world data, we observed an improved regression performance mainly for datasets from subjects with initially poor performance. With this proof-of-concept paper, we provided a generalizable regularization framework for SPoC which may serve as a starting point for implementing advanced techniques in the future.
Published: 2018

9. Speed of rapid serial visual presentation of pictures, numbers and words affects event-related potential-based detection accuracy

Author: Phillippa Payne, Fabien Lotte, Damien Coyle, Liam Maguire, Stephanie Lees, Paul McCullagh, University of Ulster, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Subjects: Male, Rapid serial visual presentation, Brain- Computer Interface, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO]Cognitive science, 0302 clinical medicine, Protocol design, EEG, [INFO.INFO-BT]Computer Science [cs]/Biotechnology, BCI, Evoked Potentials, Mathematics, General Neuroscience, 05 social sciences, Rehabilitation, Single type, brain-computer interface, Electroencephalography, Healthy Volunteers, Area Under Curve, Brain-Computer Interfaces, Calibration, Visual Perception, Female, electroencephalography, Adult, Biomedical Engineering, 050105 experimental psychology, Young Adult, 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Rapid Serial Visual Presentation, Event-related potential, Presentation duration, Internal Medicine, Humans, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], event related potentials, Event Related Potentials, Brain–computer interface, Receiver operating characteristic, business.industry, Reproducibility of Results, Pattern recognition, Multimodal image, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], Artificial intelligence, business, Photic Stimulation, 030217 neurology & neurosurgery
Abstract: Rapid serial visual presentation (RSVP) based brain-computer interfaces (BCIs) can detect target images among a continuous stream of rapidly presented images, by classifying a viewer’s event related potentials (ERPs) associated with the target and non-targets images. Whilst the majority of RSVP-BCI studies to date have concentrated on the identification of a single type of image, namely pictures , here we study the capability of RSVP-BCI to detect three different target image types: pictures, numbers and words . The impact of presentation duration (speed) i.e., 100–200ms (5–10Hz), 200–300ms (3.3–5Hz) or 300–400ms (2.5–3.3Hz), is also investigated. 2-way repeated measure ANOVA on accuracies of detecting targets from non-target stimuli (ratio 1:9) measured via area under the receiver operator characteristics curve (AUC) for ${N}={15}$ subjects revealed a significant effect of factor Stimulus-Type ( pictures, numbers, words ) (F (2,28) = 7.243, ${p} = {0.003}$ ) and for Stimulus-Duration (F (2,28) = 5.591, p = 0.011). Furthermore, there is an interaction between stimulus type and duration: F (4,56) = 4.419, ${p} = {0.004}$ ). The results indicate that when designing RSVP-BCI paradigms, the content of the images and the rate at which images are presented impact on the accuracy of detection and hence these parameters are key experimental variables in protocol design and applications, which apply RSVP for multimodal image datasets.
Published: 2019

10. Towards Adaptive Classification using Riemannian Geometry approaches in Brain-Computer Interfaces

Author: Fabien Lotte, Satyam Kumar, Florian Yger, Indian Institute of Technology Kanpur (IIT Kanpur), Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Subjects: Computer science, 0206 medical engineering, 02 engineering and technology, Riemannian geometry, Machine learning, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Remannian Geometry, BCI, Brain–computer interface, Index Terms-Remannian Geometry, business.industry, Adaptation strategies, 020601 biomedical engineering, symbols, Adaptive classifier, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], Artificial intelligence, business, Classifier (UML), computer, 030217 neurology & neurosurgery, Mental image
Abstract: International audience; The omnipresence of non-stationarity and noise in Electroencephalogram signals restricts the ubiquitous use of Brain-Computer interface. One of the possible ways to tackle this problem is to adapt the computational model used to detect and classify different mental states. Adapting the model will possibly help us to track the changes and thus reducing the effect of non-stationarities. In this paper, we present different adaptation strategies for state of the art Riemannian geometry based classifiers. The offline evaluation of our proposed methods on two different datasets showed a statistically significant improvement over baseline non-adaptive classifiers. Moreover, we also demonstrate that combining different (hybrid) adaptation strategies generally increased the performance over individual adaptation schemes. Also, the improvement in average classification accuracy for a 3-class mental imagery BCI with hybrid adaption is as high as around 17% above the baseline non-adaptive classifier.
Published: 2019

11. Brain–Computer Interface Contributions to Neuroergonomics

Author: Fabien Lotte, Raphaëlle N. Roy, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), Lotte, Fabien, Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole Nationale Supérieure d’Electronique, Informatique et Radiocommunications de Bordeaux - ENSEIRB (FRANCE), Institut National de la Recherche en Informatique et en Automatique - INRIA (FRANCE), Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Université de Bordeaux 1 (FRANCE), Université de Bordeaux 2 - Victor Segalen (FRANCE), Département Conception et conduite des véhicules Aéronautiques et Spatiaux - DCAS (Toulouse, France), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Brain-Computer Interface, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, 0206 medical engineering, Feature extraction, Stereoscopy, 02 engineering and technology, law.invention, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, [SCCO]Cognitive science, 0302 clinical medicine, [STAT.AP] Statistics [stat]/Applications [stat.AP], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, law, Human–computer interaction, [INFO.INFO-ET] Computer Science [cs]/Emerging Technologies [cs.ET], Neuroergonomics, EEG, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-BT]Computer Science [cs]/Biotechnology, Adaptation (computer science), Video game, Brain–computer interface, Signal processing, [STAT.AP]Statistics [stat]/Applications [stat.AP], Neurosciences, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [SCCO] Cognitive science, 020601 biomedical engineering, Cockpit, [INFO.INFO-BT] Computer Science [cs]/Biotechnology, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], 030217 neurology & neurosurgery
Abstract: International audience; Brain-Computer Interfaces (BCIs) are systems that can translate brain activity patterns into messages or commands for an interactive application. As such the technology used to design them, and in particular to design passive BCIs which are a new means to perform mental state monitoring, can greatly benefit the neuroergonomics field. Therefore, this chapter describes the classical structure of the brain signal processing chain employed in BCIs, notably presenting the typically used preprocessing (spatial and spectral filtering, artefact removal), feature extraction and classification algorithms. It also gives examples of the use of BCI technology for neuroergonomics applications, either offline for evaluation purposes (e.g. cockpit design or stereoscopic displays’ assessment), or online for adaptation purposes (e.g. video game difficulty level or air traffic controller display adaptation).
Published: 2019

12. SEREEGA: Simulating event-related EEG activity

Author: Juliane Pawlitzki, Laurens R. Krol, Fabien Lotte, Klaus Gramann, Thorsten O. Zander, Technische Universität Berlin (TU), Zander Laboratories, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of Technology Sydney (UTS), University of California [San Diego] (UC San Diego), University of California, Part of this work was supported by the Deutsche Forschungsgemeinschaft (grant number ZA 821/3-1), Idex Bordeaux and LabEX CPU/SysNum, and the European Research Council with the BrainConquest project (grant number ERC-2016-STG-714567), Technical University of Berlin / Technische Universität Berlin (TU), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and University of California (UC)
Subjects: Brain-Computer Interface, Sound Spectrography, Computer science, Event-Related, Ground Trut, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], Electroencephalography, 050105 experimental psychology, Set (abstract data type), 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, ddc:153, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, Humans, 0501 psychology and cognitive sciences, Computer Simulation, Evaluation, MATLAB, Set (psychology), Evoked Potentials, Brain–computer interface, computer.programming_language, Signal processing, Neurology & Neurosurgery, medicine.diagnostic_test, business.industry, Event (computing), [SCCO.NEUR]Cognitive science/Neuroscience, General Neuroscience, SIGNAL (programming language), 05 social sciences, Reproducibility of Results, Pattern recognition, Signal Processing, Computer-Assisted, Classification, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Toolbox, Artificial intelligence, ddc:004, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, Simulation, 030217 neurology & neurosurgery, Software
Abstract: Electroencephalography (EEG) is a popular method to monitor brain activity, but it can be difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings, ensuring that it is known beforehand which e ects are present in the data. As such, simulated data can be used, among other things, to assess or compare signal processing and machine learn-ing algorithms, to model EEG variabilities, and to design source reconstruction methods. In this paper, we present SEREEGA, short for Simulating Event-Related EEG Activity. SEREEGA is a MATLAB-based open-source toolbox dedicated to the generation of sim-ulated epochs of EEG data. It is modular and extensible, at initial release supporting ve different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general work ow of this toolbox, as well as a simulated data set demonstrating some of its functions.HighlightsSimulated EEG data has a known ground truth, which can be used to validate methods.We present a general-purpose open-source toolbox to simulate EEG data.It provides a single framework to simulate many different types of EEG recordings.It is modular, extensible, and already includes a number of head models and signals.It supports noise, oscillations, event-related potentials, connectivity, and more.
Published: 2018

13. A review of user training methods in brain computer interfaces based on mental tasks

Author: Léa Pillette, Fabien Lotte, Jelena Mladenović, Camille Benaroch, Camille Jeunet, Bernard N'Kaoua, Aline Roc, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Handicap Activité Cognition Santé [Bordeaux] (HACS), Université de Bordeaux (UB)-Institut National de Recherche en Informatique et en Automatique (Inria)-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM), Cognition, Langues, Langage, Ergonomie (CLLE), Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J), This work was supported by the European ResearchCouncil with project BrainConquest (grant ERC-2016-STG-714567)., Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, École Pratique des Hautes Études (EPHE), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)
Subjects: Computer science, Process (engineering), Reliability (computer networking), 0206 medical engineering, Control (management), Biomedical Engineering, [SCCO.COMP]Cognitive science/Computer science, Mental imagery, 02 engineering and technology, Instructions, Training tasks, Training (civil), User, Feedback, [SCCO]Cognitive science, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Human–computer interaction, Taxonomy (general), Humans, Learning, Mental task, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Electroencephalography (EEG), Brain–computer interface, [SCCO.NEUR]Cognitive science/Neuroscience, Brain, Reproducibility of Results, Electroencephalography, Brain-Computer Interfaces (BCI), 020601 biomedical engineering, Categorization, Brain-Computer Interfaces, [SCCO.PSYC]Cognitive science/Psychology, 030217 neurology & neurosurgery, Mental image
Abstract: Mental-tasks based brain–computer interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training—notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.
Published: 2021

14. A generic framework for adaptive EEG-based BCI training and operation

Author: Jelena Mladenović, Jérémie Mattout, Fabien Lotte, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre de recherche en neurosciences de Lyon (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Chang S. Nam, Anton Nijholt, Fabien Lotte, Mladenovic, Jelena, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, 0209 industrial biotechnology, Computer science, Computer Science - Human-Computer Interaction, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Electroencephalography, Quantitative Biology - Quantitative Methods, Field (computer science), Human-Computer Interaction (cs.HC), 03 medical and health sciences, [SCCO]Cognitive science, 020901 industrial engineering & automation, 0302 clinical medicine, [SCCO.COMP] Cognitive science/Computer science, Human–computer interaction, Taxonomy (general), medicine, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Adaptation (computer science), Quantitative Methods (q-bio.QM), [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Brain–computer interface, medicine.diagnostic_test, business.industry, Instructional design, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, [SCCO] Cognitive science, Conceptual framework, FOS: Biological sciences, Artificial intelligence, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, 030217 neurology & neurosurgery
Abstract: International audience; There are numerous possibilities and motivations for an adaptive BCI, which may not be easy to clarify and organize for a newcomer to the field. To our knowledge, there has not been any work done in classifying the literature on adaptive BCI in a comprehensive and structured way. We propose a conceptual framework, a taxonomy of adaptive BCI methods which encompasses most important approaches to fit them in such a way that a reader can clearly visualize which elements are being adapted and for what reason. In the interest of having a clear review of existing adaptive BCIs, this framework considers adaptation approaches for both the user and the machine, i.e., using instructional design observations as well as the usual machine learning techniques. This framework not only provides a coherent review of such extensive literature but also enables the reader to perceive gaps and flaws in the current BCI systems, which would hopefully bring novel solutions for an overall improvement.
Published: 2017

15. EEG Feature Extraction

Author: Marco Congedo and Fabien Lotte
Subjects: medicine.diagnostic_test, business.industry, Computer science, 0206 medical engineering, Extraction (chemistry), Pattern recognition, 02 engineering and technology, Electroencephalography, 020601 biomedical engineering, 0202 electrical engineering, electronic engineering, information engineering, medicine, 020201 artificial intelligence & image processing, Artificial intelligence, EEG feature, business, Brain–computer interface
Published: 2016

16. Human Learning for Brain–Computer Interfaces

Author: Fabien Lotte, Bernard N'Kaoua, and Camille Jeunet
Subjects: Cognitive science, 03 medical and health sciences, 0302 clinical medicine, Computer science, Psychology of learning, 020101 civil engineering, 02 engineering and technology, 030217 neurology & neurosurgery, Human learning, 0201 civil engineering, Mental image, Brain–computer interface
Published: 2016

17. A BCI challenge for the signal-processing community: considering the user in the loop

Author: Jelena Mladenović, Bernard N'Kaoua, Fabien Lotte, Léa Pillette, and Camille Jeunet
Subjects: Signal processing, Human–computer interaction, business.industry, Computer science, User modeling, Usability, User-in-the-loop, business, ENCODE, Mental calculation, Mental image, Brain–computer interface
Abstract: Electroencephalography (EEG)-based brain-computer interfaces (BCIs) have proven promising for a wide range of applications, from communication and control for motor impaired users to gaming targeted at the general public, real-time mental state monitoring and stroke rehabilitation, to name a few. Despite this promising potential, BCIs are still scarcely used outside laboratories for practical applications. The main reason preventing EEG-based BCIs from being widely used is arguably their poor usability, which is notably due to their low robustness and reliability. To operate a BCI, the user has to encode commands in his/her EEG signals, typically using mental imagery tasks, such as imagining hand movement or mental calculation. The execution of these tasks leads to specific EEG patterns, which the machine has to decode by using signal processing and machine learning. So far, to address the reliability issue of BCI, most research efforts have been focused on command decoding only. However, if the user is unable to encode commands in her EEG patterns, no signal-processing algorithm would be able to decode them. Therefore, we argue in this chapter that BCI design is not only a decoding challenge (i.e., translating EEG signals into control commands) but also a human-computer interaction challenge, which aims at ensuring the user can control the BCI. Interestingly enough, there are a number of open challenges to take the user into account, for which signal-processing and machine-learning methods could provide solutions. These challenges notably concerns (1) the modeling of the user and (2) understanding and improving how and what the user is learning. More precisely, the BCI community should first work on user modeling, i.e., modeling and updating the user's states and skills overtime from his/herEEG signals, behavior, BCI performances and possibly other sensors. The community should also identify new performance metrics-beyond classification accuracy-that could better describe users' skills at BCI control. Second, the BCI community has to understand how and what the user learns to control the BCI. This includes thoroughly identifying the features to be extracted and the classifier to be used to ensure the user's understanding of the feedback resulting from them, as well as how to present this feedback. Being able to update machine-learning parameters in a specific manner and a precise moment to favor learning without confusing the user with the ever-changeable feedback is another challenge. Finally, it is necessary to gain a clearer understanding of the reasons why mental commands are sometimes correctly decoded and sometimes not; what makes people sometimes fail at BCI control, in order to be able to guide them to do better. Overall, this chapter identifies a number of open and important challenges for the BCI community, at the user level, to which experts in machine learning and signal processing could contribute.
Published: 2018

18. Towards Artificial Learning Companions for Mental Imagery-based Brain-Computer Interfaces

Author: Léa Pillette, Camille Jeunet, Kambou, Roger N., Kaoua, Bernard N., Fabien Lotte, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Institut Polytechnique de Bordeaux (Bordeaux INP), Chair in Brain-Machine Interface [Geneva] (CNBI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-MEDIA ET INTERACTIONS (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire de recherche en Gestion, Diffusion et Acquisition des Connaissances (Laboratoire GDAC), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Technologie des langages de programmation pour les services de communication (PHOENIX-POST), Inria Bordeaux - Sud-Ouest, Laboratoire Handicap Activité Cognition et Système Nerveux (HACS), Université de Bordeaux (UB), Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)
Subjects: FOS: Computer and information sciences, Brain-Computer Interface, Learning Companion, Computer Science - Human-Computer Interaction, Social Feedback, Affective Feedback, Human-Computer Interaction (cs.HC), ACM: K.: Computing Milieux/K.3: COMPUTERS AND EDUCATION/K.3.1: Computer Uses in Education, [SCCO]Cognitive science, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.2: Design Tools and Techniques/D.2.2.12: User interfaces, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval/H.3.3.3: Relevance feedback, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Affective Feed-back
Abstract: International audience; Mental Imagery based Brain-Computer Interfaces (MI-BCI) enable their users to control an interface, e.g., a prosthesis, by performing mental imagery tasks only, such as imagining a right arm movement while their brain activity is measured and processed by the system. Designing and using a BCI requires users to learn how to produce different and stable patterns of brain activity for each of the mental imagery tasks. However, current training protocols do not enable every user to acquire the skills required to use BCIs. These training protocols are most likely one of the main reasons why BCIs remain not reliable enough for wider applications outside research laboratories. Learning companions have been shown to improve training in different disciplines, but they have barely been explored for BCIs so far. This article aims at investigating the potential benefits learning companions could bring to BCI training by improving the feedback, i.e., the information provided to the user, which is primordial to the learning process and yet have proven both theoretically and practically inadequate in BCI. This paper first presents the potentials of BCI and the limitations of current training approaches. Then, it reviews both the BCI and learning companion literature regarding three main characteristics of feedback: its appearance, its social and emotional components and its cognitive component. From these considerations, this paper draws some guidelines, identify open challenges and suggests potential solutions to design and use learning companions for BCIs.; Les interfaces cerveau-ordinateur (ICO) exploitant l'imagerie men-tale permettent à leurs utilisateurs d'envoyer des commandes à une interface, une prothèse par exemple, uniquement en réalisant des tâches d'imagerie mentale, tel qu'imaginer son bras droit bouger. Lors de la réalisation de ces tâches, l'activité cérébrale des utilisa-teurs est enregistrée et analysée par le système. Afin de pouvoir utiliser ces interfaces, les utilisateurs doivent apprendre à produire différents motifs d'activité cérébrale stables pour chacune des tâches d'imagerie mentale. Toutefois, les protocoles d'entraînement ex-istants ne permettent pas à tous les utilisateurs de maîtriser les compétences nécessaires à l'utilisation des ICO. Ces protocoles d'entraînements font très probablement partie des raisons princi-pales pour lesquelles les ICO manquent de fiabilité et ne sont pas plus utilisées en dehors des laboratoires de recherche. Or, les com-pagnons d'apprentissage, qui ont déjà permis d'améliorer l'efficacité d'apprentissage pour différentes disciplines, sont encore à peine étudiés pour les ICO. L'objectif de cet article est donc d'explorer les différents avantages qu'ils pourraient apporter à l'entraînement aux ICO en améliorant le retour fait à l'utilisateur, c'est-à-dire les informations fournies concernant la tâche. Ces dernières sont pri-mordiales à l'apprentissage et pourtant, il a été montré qu'à la fois théoriquement et en pratique ces dernières étaient inadéquates. Tout d'abord, seront présentés dans l'article les potentiels des ICO et les limitations des protocoles d'entraînement actuels. Puis, une revue de la littérature des ICO ainsi que des compagnons d'apprentissage est réalisée concernant trois caractéristiques principales du retour utilisateur, c'est-à-dire son apparence, ses composantes sociale et émotionnelle et enfin sa composante cognitive. À partir de ces considérations, ce papier fournit quelques recommandations, iden-tifie des défis à relever et suggère des solutions potentielles pour concevoir et utiliser des compagnons d'apprentissage en ICO.
Published: 2018

19. Defining and quantifying users' mental imagery-based BCI skills: a first step

Author: Camille Jeunet, Fabien Lotte, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-MEDIA ET INTERACTIONS (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Chair in Brain-Machine Interface [Geneva] (CNBI), Ecole Polytechnique Fédérale de Lausanne (EPFL), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)
Subjects: Imagery, Psychotherapy, Computer science, 0206 medical engineering, Biomedical Engineering, 02 engineering and technology, Matlab code, Electroencephalography, Machine learning, computer.software_genre, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], Eeg patterns, 03 medical and health sciences, Cellular and Molecular Neuroscience, [SCCO]Cognitive science, 0302 clinical medicine, InformationSystems_MODELSANDPRINCIPLES, Eeg data, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, medicine, Humans, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-BT]Computer Science [cs]/Biotechnology, Brain–computer interface, Training set, medicine.diagnostic_test, business.industry, 020601 biomedical engineering, Brain-Computer Interfaces, Imagination, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, Artificial intelligence, business, computer, Classifier (UML), 030217 neurology & neurosurgery, Psychomotor Performance, Mental image
Abstract: International audience; Objective: While promising for many applications, Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are still scarcely used outside laboratories , due to a poor reliability. It is thus necessary to study and fix this reliability issue. Doing so requires the use of appropriate reliability metrics to quantify both the classification algorithm and the BCI user's performances. So far, Classification Accuracy (CA) is the typical metric used for both aspects. However, we argue in this paper that CA is a poor metric to study BCI users' skills. Here, we propose a definition and new metrics to quantify such BCI skills for Mental Imagery (MI) BCIs, independently of any classification algorithm. Approach: We first show in this paper that CA is notably unspecific, discrete, training data and classifier dependent, and as such may not always reflect successful self-modulation of EEG patterns by the user. We then propose a definition of MI-BCI skills that reflects how well the user can self-modulate EEG patterns, and thus how well he could control an MI-BCI. Finally, we propose new performance metrics, classDis, restDist and classStab that specifically measure how distinct and stable the EEG patterns produced by the user are, independently of any classifier. Main results: By re-analyzing EEG data sets with such new metrics, we indeed confirmed that CA may hide some increase in MI-BCI skills or hide the user inability to self-modulate a given EEG pattern. On the other hand, our new met-rics could reveal such skill improvements as well as identify when a mental task performed by a user was no different than rest EEG. Significance: Our results showed that when studying MI-BCI users' skills, CA should be used with care, and complemented with metrics such as the new ones proposed. Our results also stressed the need to redefine BCI user training by considering the different BCI subskills and their measures. To promote the complementary use of our new metrics, we provide the Matlab code to compute them for free and open-source.
Published: 2018

20. A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update

Author: Florian Yger, Laurent Bougrain, Alain Rakotomamonjy, Andrzej Cichocki, Fabien Lotte, Maureen Clerc, Marco Congedo, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Analysis and modeling of neural systems by a system neuroscience approach (NEUROSYS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Skolkovo Institute of Science and Technology [Moscow] (Skoltech), Nicolaus Copernicus University [Toruń], Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Equipe Apprentissage (DocApp - LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), and Normandie Université (NU)
Subjects: Signal processing, Time Factors, Computer science, Feature extraction, Biomedical Engineering, 02 engineering and technology, Adaptive classifiers, Tensors, Machine learning, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, Animals, Humans, EEG, Riemannian geometry, BCI, Brain–computer interface, Spatial filtering, business.industry, Deep learning, Brain, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Signal Processing, Computer-Assisted, Electroencephalography, Linear discriminant analysis, Classification, Random forest, Transfer learning, Statistical classification, ComputingMethodologies_PATTERNRECOGNITION, Brain-Computer Interfaces, 020201 artificial intelligence & image processing, Artificial intelligence, business, Transfer of learning, computer, Classifier (UML), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, 030217 neurology & neurosurgery
Abstract: International audience; Objective: Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. Approach: We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. Main results: We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. Significance: This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these Review of Classification Algorithms for EEG-based BCI 2 methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.
Published: 2018

21. Mind the Traps! Design Guidelines for Rigorous BCI Experiments

Author: Jérémie Mattout, Fabien Lotte, Camille Jeunet, Catharina Zich, Stefan Debener, and Reinhold Scherer
Subjects: business.industry, Computer science, Artificial intelligence, business, Brain–computer interface
Published: 2018

22. Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review

Author: Jean-Marie Batail, Pierre Philip, Fabien Lotte, Jean-Arthur Micoulaud Franchi, Camille Jeunet, Chair in Brain-Machine Interface [Geneva] (CNBI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-MEDIA ET INTERACTIONS (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Comportement et noyaux gris centraux = Behavior and Basal Ganglia [Rennes], Université de Rennes (UR)-Université européenne de Bretagne - European University of Brittany (UEB)-CHU Pontchaillou [Rennes]-Institut des Neurosciences Cliniques de Rennes = Institute of Clinical Neurosciences of Rennes (INCR), Sommeil, Addiction et Neuropsychiatrie [Bordeaux] (SANPSY), Université de Bordeaux (UB)-CHU de Bordeaux Pellegrin [Bordeaux]-Centre National de la Recherche Scientifique (CNRS), ANR-15-CE23-0013-01, Agence Nationale de la Recherche, ERC-2016-STG-714567, H2020 European Research Council, Swiss National Foundation, ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université européenne de Bretagne - European University of Brittany (UEB)-CHU Pontchaillou [Rennes]-Institut des Neurosciences Cliniques de Rennes (INCR), and Sommeil, Attention et Neuropsychiatrie [Bordeaux] (SANPSY)
Subjects: Process (engineering), adaptation, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, brain–computer interfaces, Humans, 0501 psychology and cognitive sciences, Relevance (information retrieval), EEG, Clinical efficacy, Adaptation (computer science), Brain–computer interface, training, Mental Disorders, General Neuroscience, 05 social sciences, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Cognition, neurofeedback efficacy, Neurofeedback, cognitive profile, personality, Brain-Computer Interfaces, Psychology, 030217 neurology & neurosurgery, Cognitive psychology
Abstract: International audience; In their recent paper, Alkoby et al. (2017) provide the readership with an extensive and very insightful review of the factors influencing NeuroFeedback (NF) performance. These factors are drawn from both the NF literature and the Brain-Computer Interface (BCI) literature. Our short review aims to complement Alkoby et al.'s review by reporting recent additions to the BCI literature. The object of this paper is to highlight this literature and discuss its potential relevance and usefulness to better understand the processes underlying NF and further improve the design of clinical trials assessing NF efficacy. Indeed, we are convinced that while NF and BCI are fundamentally different in many ways, both the BCI and NF communities could reach compelling achievements by building upon one another. By reviewing the recent BCI literature, we identified three types of factors that influence BCI performance: task-specific, cognitive/motivational and technology-acceptance-related factors. Since BCIs and NF share a common goal (i.e., learning to modulate specific neurophysiological patterns), similar cognitive and neurophysiological processes are likely to be involved during the training process. Thus, the literature on BCI training may help (1) to deepen our understanding of neurofeedback training processes and (2) to understand the variables that influence the clinical efficacy of NF. This may help to properly assess and/or control the influence of these variables during randomized controlled trials.
Published: 2018
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23. EEG neurofeedback research: A fertile ground for psychiatry?

Author: Lorraine Perronet, Thomas Fovet, Aurore Hakoun, François Cabestaing, C. Daudet, Jean-Arthur Micoulaud-Franchi, François Vialatte, Léa Pillette, Tomas Ros, Emmanuel Maby, Camille Jeunet, Mélanie Fouillen, Dominique Drapier, Jelena Mladenović, Jean-Marie Batail, Renaud Jardri, Stéphanie Bioulac, Jérémie Mattout, Fabien Lotte, Takfarinas Medani, Comportement et noyaux gris centraux = Behavior and Basal Ganglia [Rennes], Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université européenne de Bretagne - European University of Brittany (UEB)-CHU Pontchaillou [Rennes]-Institut des Neurosciences Cliniques de Rennes (INCR), Sommeil, Attention et Neuropsychiatrie [Bordeaux] (SANPSY), Université de Bordeaux (UB)-CHU de Bordeaux Pellegrin [Bordeaux]-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB), Centre de recherche en neurosciences de Lyon (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193 (SCALab), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Laboratoire Plasticité du Cerveau Brain Plasticity (UMR 8249) (PdC), Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Ecole Polytechnique Fédérale de Lausanne (EPFL), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Popular interaction with 3d content (Potioc), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-MEDIA ET INTERACTIONS (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), University medical center and campus biotech Geneva, This work was supported by the French National Research Agency within the REBEL project (grant ANR-15-CE23-0013-01), the European Research Council with the Brain Conquest project (grant ERC-2016-STG-714567), the Inria Project-Lab BCI-Lift and the EPFL/Inria International Lab, BCI-Lift, EPFL-INRIA, ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), Université de Rennes (UR)-Université européenne de Bretagne - European University of Brittany (UEB)-CHU Pontchaillou [Rennes]-Institut des Neurosciences Cliniques de Rennes = Institute of Clinical Neurosciences of Rennes (INCR), Sommeil, Addiction et Neuropsychiatrie [Bordeaux] (SANPSY), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 (SCALab), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)
Subjects: medicine.medical_specialty, Neurophysiology, Electroencephalography, 03 medical and health sciences, 0302 clinical medicine, Arts and Humanities (miscellaneous), Neuroplasticity, medicine, Learning, Training, Humans, EEG, Psychiatry, Brain–computer interface, medicine.diagnostic_test, Cognitive Behavioral Therapy, Mental Disorders, Cognition, Neurofeedback, 3. Good health, 030227 psychiatry, ddc:616.8, Psychiatry and Mental health, Hebbian theory, Psychophysiology, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Psychology, Neurocognitive
Abstract: International audience; The clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human–computer interaction that enables subjects to train voluntarily and modify functional biomarkers that are related to a defined mental disorder. We investigate three kinds of factors related to this definition of neurofeedback. We focus this article on EEG NFB. The first part of the paper investigates neurophysiological factors underlying the brain mechanisms driving NFB training and learning to modify a functional biomarker voluntarily. Two kinds of neuroplasticity involved in neurofeedback are analyzed Hebbian neuroplasticity, i.e. long-term modification of neural membrane excitability and/or synaptic potentiation, and homeostatic neuroplasticity, i.e. homeostasis attempts to stabilize network activity. The second part investigates psychophysiological factors related to the targeted biomarker. It is demonstrated that neurofeedback involves clearly defining which kind of relationship between EEG biomarkers and clinical dimensions (symptoms or cognitive processes) is to be targeted. A nomenclature of accurate EEG biomarkers is proposed in the form of a short EEG encyclopedia (EEGcopia). The third part investigates human–computer interaction factors for optimizing NFB training and learning during the closed loop interaction. A model is proposed to summarize the different features that should be controlled to optimize learning. The need for accurate and reliable metrics of training and learning in line with human–computer interaction is also emphasized, including targeted biomarkers and neuroplasticity. All these factors related to neurofeedback show that it can be considered as a fertile ground for innovative research in psychiatry.
Published: 2017

24. Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces

Author: Fabien Lotte, Lotte, Fabien, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Popular interaction with 3d content (Potioc), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Subjects: [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Calibration (statistics), 02 engineering and technology, Electroencephalography, Machine learning, computer.software_genre, Index Terms—Brain-Computer Interfaces (BCI), Regularization (mathematics), 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-BT]Computer Science [cs]/Biotechnology, Electrical and Electronic Engineering, signal processing, Brain–computer interface, Signal processing, Training set, medicine.diagnostic_test, business.industry, ElectroEncephaloGraphy (EEG), [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], calibration, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], small sample settings, machine learning, [INFO.INFO-BT] Computer Science [cs]/Biotechnology, A priori and a posteriori, Signal processing algorithms, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract: International audience; One of the major limitations of Brain-Computer Interfaces (BCI) is their long calibration time, which limits their use in practice, both by patients and healthy users alike. Such long calibration times are due to the large between-user variability and thus to the need to collect numerous training electroencephalography (EEG) trials for the machine learning algorithms used in BCI design. In this paper, we first survey existing approaches to reduce or suppress calibration time, these approaches being notably based on regularization, user-to-user transfer, semi-supervised learning and a-priori physiological information. We then propose new tools to reduce BCI calibration time. In particular, we propose to generate artificial EEG trials from the few EEG trials initially available, in order to augment the training set size. These artificial EEG trials are obtained by relevant combinations and distortions of the original trials available. We propose 3 different methods to do so. We also propose a new, fast and simple approach to perform user-to-user transfer for BCI. Finally, we study and compare offline different approaches, both old and new ones, on the data of 50 users from 3 different BCI data sets. This enables us to identify guidelines about how to reduce or suppress calibration time for BCI.
Published: 2015

25. Recreational Applications of OpenViBE: Brain Invaders and Use-the-Force

Author: Alexandre Barachant, Anton Andreev, Fabien Lotte, Marco Congedo, GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA-Services (GIPSA-Services), Weill Medical College of Cornell University [New York], Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Popular interaction with 3d content (Potioc), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Maureen Clerc, Laurent Bougrai, Fabien Lotte, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Subjects: OpenVibe, business.industry, Computer science, [SCCO.NEUR]Cognitive science/Neuroscience, 0206 medical engineering, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Use-The-Force, 020601 biomedical engineering, 030218 nuclear medicine & medical imaging, OpenViBE, 03 medical and health sciences, Brain Invaders, 0302 clinical medicine, Human–computer interaction, Artificial intelligence, P300, BCI, business, Recreation, ERP, Brain–computer interface
Abstract: International audience; This chapter aims at providing the reader with two examples of open-source BCI-games that work with the OpenViBE platform. These two games are “Brain Invaders” and “Use-The-Force!” and are representative examples of two types of BCI: ERP-based BCI and oscillatory activity-based BCI. This chapter presents the principle, design and evaluation of these games, as well as how they are implemented in practice within OpenViBE. This aims at providing the interested readers with a practical basis to design their own BCI-based games.
Published: 2016

26. When HCI Meets Neurotechnologies

Author: Jérémy Frey, Léa Pillette, Fabien Lotte, Jelena Mladenović, Camille Jeunet, Lotte, Fabien, Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle - - REBEL2015 - ANR-15-CE23-0013 - AAPG2015 - VALID, and Boosting Brain-Computer Communication with high Quality User Training - BrainConquest - - H2020 Pilier ERC2017-07-01 - 2022-06-30 - 714567 - VALID
Subjects: Ethics ACM Classification Keywords H52 [User Interfaces]: Evaluation/methodology, OpenVibe, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Interface (computing), 02 engineering and technology, [INFO] Computer Science [cs], computer.software_genre, Machine Learning, Software, Human–computer interaction, [INFO.INFO-ET] Computer Science [cs]/Emerging Technologies [cs.ET], 0202 electrical engineering, electronic engineering, information engineering, Neuroergonomics, EEG, Session (computer science), Brain–computer interface, Multimedia, business.industry, [SCCO.NEUR] Cognitive science/Neuroscience, Neuroer- gonomics, 020207 software engineering, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Author Keywords BCI, [INFO.INFO-BT] Computer Science [cs]/Biotechnology, Signal Processing, [SCCO.PSYC] Cognitive science/Psychology, 020201 artificial intelligence & image processing, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer
Abstract: The additional fee must be paid to ACM. This text field is large enough to hold the appropriate release statement assuming it is single spaced in a sans-serif 7 point font. Every submission will be assigned their own unique DOI string to be included here. Abstract Brain-Computer Interfaces (BCIs) have brought new, exciting and promising perspectives of interaction. On the one hand, active BCIs enable users to control applications (such as assistive technologies or video games) using their brain activity alone. On the other hand, passive BCIs bring the possibility of adapting an application/interface based on users' mental states. In this course, we first aim at introducing BCIs to the HCI community and to discuss how BCI-based applications could benefit HCI. Then, in a practical session, we will propose all participants to implement their own BCI, in a very simple way, using the free Open-ViBE software. Finally, we will have discussions about what is possible or not with BCIs, what are their pros and cons.
Published: 2017

27. Scientific Outreach with Teegi, a Tangible EEG Interface to Talk about Neurotechnologies

Author: Jérémy Frey, Stéphanie Fleck, Hugo Germain, Thibault Lainé, Martin Hachet, Fabien Lotte, Renaud Gervais, Maxime Duluc, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Ullo, Psychologie Ergonomique et Sociale pour l'Expérience utilisateurs (PErSEUs), Université de Lorraine (UL), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Brain activity and meditation, Computer science, Interface (computing), Computer Science - Human-Computer Interaction, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning/I.2.6.4: Knowledge acquisition, 02 engineering and technology, Electroencephalography, ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI)/H.5.2: User Interfaces/H.5.2.7: Interaction styles (e.g., commands, menus, forms, direct manipulation), Human-Computer Interaction (cs.HC), Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Tangible Interaction, 0501 psychology and cognitive sciences, EEG, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], BCI, 050107 human factors, Brain–computer interface, Avatar, medicine.diagnostic_test, [SCCO.NEUR]Cognitive science/Neuroscience, 05 social sciences, ACM: H.: Information Systems/H.1: MODELS AND PRINCIPLES/H.1.2: User/Machine Systems/H.1.2.1: Human information processing, 020207 software engineering, ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI)/H.5.1: Multimedia Information Systems/H.5.1.1: Artificial, augmented, and virtual realities, Outreach, Scientific Outreach
Abstract: International audience; Teegi is an anthropomorphic and tangible avatar exposing a users' brain activity in real time. It is connected to a device sensing the brain by means of electroencephalog-raphy (EEG). Teegi moves its hands and feet and closes its eyes along with the person being monitored. It also displays on its scalp the associated EEG signals, thanks to a semi-spherical display made of LEDs. Attendees can interact directly with Teegi – e.g. move its limbs – to discover by themselves the underlying brain processes. Teegi can be used for scientific outreach to introduce neurotechnologies in general and brain-computer interfaces (BCI) in particular.
Published: 2017

28. A Review of Rapid Serial Visual Presentation-based Brain-Computer Interfaces

Author: Fabien Lotte, Natalie Dayan, Liam Maguire, Stephanie Lees, Paul McCullagh, Damien Coyle, Hubert Cecotti, Faculty of Computing and Engineering [University of Ulster], University of Ulster, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Time Factors, Brain-Computer Interface, Computer science, media_common.quotation_subject, 0206 medical engineering, Biomedical Engineering, 02 engineering and technology, Electroencephalography, computer.software_genre, 03 medical and health sciences, Cellular and Molecular Neuroscience, Presentation, [SCCO]Cognitive science, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Event-related potential, Rapid Serial Visual Presentation, medicine, Humans, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Event Related Potentials, Brain–computer interface, media_common, medicine.diagnostic_test, Multimedia, Event (computing), [SCCO.NEUR]Cognitive science/Neuroscience, Brain, 020601 biomedical engineering, Identification (information), Rapid serial visual presentation, Brain-Computer Interfaces, Human visual system model, Evoked Potentials, Visual, computer, 030217 neurology & neurosurgery, Photic Stimulation
Abstract: International audience; Rapid serial visual presentation (RSVP) combined with the detection of event related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs) measured non-invasively with electroencephalography (EEG) can be associated with infrequent targets amongst a stream of images. Human-machine symbiosis may be augmented by enabling human interaction with a computer, without overt movement, and/or enable optimization of image/information sorting processes involving humans. Features of the human visual system impact on the success of the RSVP paradigm, but pre-attentive processing supports the identification of target information post presentation of the information by assessing the co-occurrence or time-locked EEG potentials. This paper presents a comprehensive review and evaluation of the limited but significant literature on research in RSVP-based brain-computer interfaces (BCIs). Applications that use RSVP-based BCIs are categorized based on display mode and protocol design, whilst a range of factors influencing ERP evocation and detection are analyzed. Guidelines for using the RSVP-based BCI paradigms are recommended, with a view to further standardizing methods and enhancing the inter-relatability of experimental design to support future research and the use of RSVP-based BCIs in practice.
Published: 2017

29. Riemannian approaches in Brain-Computer Interfaces: a review

Author: Fabien Lotte, Florian Yger, Maxime Berar, Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Equipe Apprentissage (DocApp - LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Lotte, Fabien, and Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle - - REBEL2015 - ANR-15-CE23-0013 - AAPG2015 - VALID
Subjects: 02 engineering and technology, Computer Science::Human-Computer Interaction, Electroencephalography, computer.software_genre, source extraction, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Index Terms—Riemannian geometry, subspaces, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-BT]Computer Science [cs]/Biotechnology, Mathematics, Signal processing, medicine.diagnostic_test, General Neuroscience, Rehabilitation, Signal Processing, Computer-Assisted, Equipment Design, Covariance, Manifold, classification, Brain-Computer Interfaces, Outlier, symbols, 020201 artificial intelligence & image processing, Algorithms, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Biomedical Engineering, Riemannian geometry, Machine learning, 03 medical and health sciences, symbols.namesake, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Internal Medicine, medicine, Humans, Electroencephalography (EEG), Brain–computer interface, Brain-Computer Inter, Quantitative Biology::Neurons and Cognition, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, covariance matrices, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Models, Theoretical, face (BCI), [INFO.INFO-BT] Computer Science [cs]/Biotechnology, ComputingMethodologies_PATTERNRECOGNITION, Artificial intelligence, business, Classifier (UML), computer, 030217 neurology & neurosurgery
Abstract: International audience; Although promising from numerous applications, current Brain-Computer Interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and the non-stationarity of ElectroEncephaloGraphic (EEG) signals, they require long calibration times and are not reliable. Thus, new approaches and tools, notably at the EEG signal processing and classification level, are necessary to address these limitations. Riemannian approaches, spearheaded by the use of covariance matrices, are such a very promising tool slowly adopted by a growing number of researchers. This article, after a quick introduction to Riemannian geometry and a presentation of the BCI-relevant manifolds, reviews how these approaches have been used for EEG-based BCI, in particular for feature representation and learning, classifier design and calibration time reduction. Finally, relevant challenges and promising research directions for EEG signal classification in BCIs are identified, such as feature tracking on manifold or multi-task learning.
Published: 2017

30. Heading for new shores! Overcoming pitfalls in BCI design

Author: Melanie Fried-Oken, Ricardo Chavarriaga, Reinhold Scherer, Fabien Lotte, Sonja C. Kleih, Ecole Polytechnique Fédérale de Lausanne (EPFL), Oregon Health and Science University [Portland] (OHSU), Julius-Maximilians-Universität Würzburg (JMU), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Popular interaction with 3d content (Potioc), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Graz University of Technology [Graz] (TU Graz), ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), Julius-Maximilians-Universität Würzburg [Wurtzbourg, Allemagne] (JMU), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest
Subjects: Heading (navigation), Computer science, 0206 medical engineering, pitfalls, Biomedical Engineering, 02 engineering and technology, Article, Communication device, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, Behavioral Neuroscience, brain-computer interfaces, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Human–computer interaction, Research community, user centered design, EEG, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Electrical and Electronic Engineering, BCI, [INFO.INFO-BT]Computer Science [cs]/Biotechnology, user training, signal processing, User-centered design, Brain–computer interface, publication bias, reporting, artefacts, [SCCO.NEUR]Cognitive science/Neuroscience, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020601 biomedical engineering, Human-Computer Interaction, Multiple factors, [SCCO.PSYC]Cognitive science/Psychology, Concrete research, Artifacts, 030217 neurology & neurosurgery
Abstract: International audience; Research in brain-computer interfaces has achieved impressive progress towards implementing assistive technologies for restoration or substitution of lost motor capabilities, as well as supporting technologies for able-bodied subjects. Notwithstanding this progress, effective translation of these interfaces from proof-of concept prototypes into reliable applications remains elusive. As a matter of fact, most of the current BCI systems cannot be used independently for long periods of time by their intended end-users. Multiple factors that impair achieving this goal have already been identified. However, it is not clear how do they affect the overall BCI performance or how they should be tackled. This is worsened by the publication bias where only positive results are disseminated, preventing the research community from learning from its errors. This paper is the result of a workshop held at the 6th International BCI meeting in Asilomar. We summarize here the discussion on concrete research avenues and guidelines that may help overcoming common pitfalls and make BCIs become a useful alternative communication device.
Published: 2016

31. Guest editorial: Brain/neuronal computer games interfaces and interaction

Author: Damien Coyle, Fabien Lotte, Anton Nijholt, and Jose C. Principe
Subjects: HMI-HF: Human Factors, HMI-CI: Computational Intelligence, Exploit, Computer science, Multi-modal interaction, computer.software_genre, Artificial Intelligence, Human–computer interaction, Control, Biosignal, EEG, Electrical and Electronic Engineering, Brain–computer interface, EWI-23357, Multimedia, ComputingMilieux_PERSONALCOMPUTING, IR-86470, Computer game, multi-brain interfaces, Control and Systems Engineering, Brain-Computer Interfaces, Snapshot (computer storage), User interface, Games, computer, METIS-297644, Software
Abstract: Nowadays brainwave or electroencephalogram (EEG) controlled games controllers are adding new options to satisfy the continual demand for new ways to interact with games, following trends such as the Nintendo® Wii, Microsoft® Kinect and Playstation® Move which are based on accelerometers and motion capture. EEG-based brain-computer games interaction are controlled through brain-computer interface (BCI) technology which requires sophisticated signal processing to produce a low communication bandwidth with few degrees of freedom and relatively inaccurate and unstable control signal. Recently entertainment and gaming have become a popular application focus for BCI researchers and games developers. This special issue was therefore solicited to gain insights into new biosignal processing algorithms tested in gaming applications and gaming applications which exploit BCI and neural signals to enhance game play experience and player motivation, be the players able-bodied or physically impaired.
Published: 2013

32. Two Brains, One Game: Design and Evaluation of a Multiuser BCI Video Game Based on Motor Imagery

Author: Fabien Lotte, Laurent Bonnet, and Anatole Lécuyer
Subjects: Collaborative software, Multimedia, business.industry, Computer science, Football, computer.software_genre, Hand movements, Game design, Motor imagery, Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering, business, computer, Video game, Software, Brain–computer interface
Abstract: How can we connect two brains to a video game by means of a brain-computer interface (BCI), and what will happen when we do so? How will the two users behave, and how will they perceive this novel common experience? In this paper, we are concerned with the design and evaluation of multiuser BCI applications. We created a multiuser videogame called BrainArena in which two users can play a simple football game by means of two BCIs. They can score goals on the left or right side of the screen by simply imagining left or right hand movements. To add another interesting element, the gamers can play in a collaborative manner (their two mental activities are combined to score in the same goal), or in a competitive manner (the gamers must push the ball in opposite directions). Two experiments were conducted to evaluate the performance and subjective experience of users in the different conditions. In the first experiment, we compared a single-user situation with one multiuser situation: the collaborative task. Experiment 1 showed that multiuser conditions are significantly preferred, in terms of fun and motivation, compared to the single-user condition. The performance of some users was even significantly improved in the multiuser condition. A subset of well-performing subjects was involved in the second experiment, where we added the competitive task. Experiment 2 suggested that competitive and collaborative conditions may lead to similar performances and motivations. However, the corresponding gaming experiences can be perceived differently among the participants. Taken together our results suggest that multiuser BCI applications can be operational, effective, and more engaging for participants.
Published: 2013

33. Brain-Computer Interfaces 2

Author: Laurent Bougrain, Fabien Lotte, and Maureen Clerc
Subjects: 03 medical and health sciences, 0302 clinical medicine, Human–computer interaction, Computer science, 05 social sciences, 0501 psychology and cognitive sciences, 030217 neurology & neurosurgery, 050105 experimental psychology, Brain–computer interface
Published: 2016

34. Brain–Computer Interfaces 1

Author: Fabien Lotte, Maureen Clerc, and Laurent Bougrain
Subjects: 03 medical and health sciences, 0302 clinical medicine, Human–computer interaction, Computer science, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, 030217 neurology & neurosurgery, Brain–computer interface
Published: 2016

35. Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study

Author: Emilie Jahanpour, Camille Jeunet, Fabien Lotte, Université de Bordeaux (UB), Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)
Subjects: Adult, Male, spatial abilities, Computer science, Spatial ability, Biomedical Engineering, training protocols, Context (language use), Spatial memory, 050105 experimental psychology, Mental rotation, 03 medical and health sciences, Cellular and Molecular Neuroscience, Young Adult, 0302 clinical medicine, Human–computer interaction, Humans, Learning, 0501 psychology and cognitive sciences, Motor skill, Reliability (statistics), Brain–computer interface, Protocol (science), business.industry, 05 social sciences, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Reproducibility of Results, Electroencephalography, Signal Processing, Computer-Assisted, Brain Computer Interface BCI, Motor Skills, Brain-Computer Interfaces, Space Perception, Imagination, Female, Artificial intelligence, business, 030217 neurology & neurosurgery, performance, Algorithms, Psychomotor Performance, mental rotation, Spatial Navigation
Abstract: International audience; Objective While promising, ElectroEncephaloGraphy based Brain-Computer Interfaces (BCIs) remain barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to what extent standard training protocols impact users' Motor-Imagery based BCI (MI-BCI) control performance. Approach We performed two experiments. The first consisted in evaluating a standard BCI training protocol efficiency for the acquisition of non-BCI related skills in a BCI-free context, which enabled to rule out the possible impact of BCIs on the training outcome. Thus, participants (N=54) were asked to perform simple motor-tasks. The second experiment aimed at measuring the correlations between motor-task and MI-BCI performances. The 10 best and 10 worst performers of the first study were recruited for an MI-BCI experiment during which they had to learn to perform 2 MI-tasks. We also assessed users' spatial abilities and pre-training mu rhythm amplitude, as both have been related to MI-BCI performance in the literature. Main Results Around 17% of the participants were unable to learn to perform the motor-tasks, which is close to the BCI-illiteracy rate. It suggests that standard training protocols are suboptimal for skill-teaching. No correlation was found between motor-task and MI-BCI performance. However, spatial abilities played an important role in MI-BCI performance. Besides, once the " spatial ability" covariable had been controlled for, using an ANCOVA, it appeared that participants who faced difficulty during the first experiment improved during the second while the others did not. Significance These studies suggest that 1) standard MI-BCI training protocols are suboptimal for skill-teaching, 2) spatial abilities are confirmed to impact MI-BCI performances and 3) when faced with difficult pre-training, subjects seem to explore more strategies and therefore learn better.
Published: 2016

36. Advances in user-training for mental-imagery-based BCI control

Author: Fabien Lotte, Bernard N'Kaoua, and Camille Jeunet
Subjects: Neural correlates of consciousness, Sense of agency, media_common.quotation_subject, 05 social sciences, Cognition, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Personality, 0501 psychology and cognitive sciences, Psychology, Control (linguistics), Social psychology, Functional illiteracy, 030217 neurology & neurosurgery, Mental image, Brain–computer interface, Cognitive psychology, media_common
Abstract: While being very promising for a wide range of applications, Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) remain barely used outside laboratories, notably due to the difficulties users encounter when attempting to control them. Indeed, 10 to 30% of users are unable to control MI-BCIs (so-called " BCI illiteracy ") while only a small proportion reach acceptable control abilities. This huge inter-user variability has led the community to investigate potential predictors of performance related to users' personality and cognitive profile. Based on a literature review, we propose a classification of these MI-BCI performance predictors into three categories representing high-level cognitive concepts: (1) users' relationship with the technology (including the notions of computer-anxiety and sense of agency), (2) attention and (3) spatial abilities. We detail these concepts and their neural correlates in order to better understand their relationship with MI-BCI user-training. Consequently, we propose, by way of future prospects, some guidelines to improve MI-BCI user-training.
Published: 2016

37. Exploring two novel features for EEG-based brain–computer interfaces: Multifractal cumulants and predictive complexity

Author: Nicolas Brodu, Anatole Lécuyer, Fabien Lotte, Lotte, Fabien, Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Virtual Reality for Improved Innovative Immersive Interaction (VR4I), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Cachan (ENS Cachan)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Cachan (ENS Cachan)-Inria Rennes – Bretagne Atlantique
Subjects: [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Cognitive Neuroscience, Speech recognition, 0206 medical engineering, Feature extraction, FOS: Physical sciences, 02 engineering and technology, Electroencephalography, Field (computer science), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, medicine, Cumulant, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Brain–computer interface, medicine.diagnostic_test, business.industry, SIGNAL (programming language), Probability and statistics, Pattern recognition, Multifractal system, 020601 biomedical engineering, Computer Science Applications, [NLIN.NLIN-CD] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD], Feature (computer vision), Quantitative Biology - Neurons and Cognition, Physics - Data Analysis, Statistics and Probability, FOS: Biological sciences, [NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD], Neurons and Cognition (q-bio.NC), 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Data Analysis, Statistics and Probability (physics.data-an)
Abstract: In this paper, we introduce two new features for the design of electroencephalography (EEG) based Brain-Computer Interfaces (BCI): one feature based on multifractal cumulants, and one feature based on the predictive complexity of the EEG time series. The multifractal cumulants feature measures the signal regularity, while the predictive complexity measures the difficulty to predict the future of the signal based on its past, hence a degree of how complex it is. We have conducted an evaluation of the performance of these two novel features on EEG data corresponding to motor-imagery. We also compared them to the most successful features used in the BCI field, namely the Band-Power features. We evaluated these three kinds of features and their combinations on EEG signals from 13 subjects. Results obtained show that our novel features can lead to BCI designs with improved classification performance, notably when using and combining the three kinds of feature (band-power, multifractal cumulants, predictive complexity) together., Comment: Updated with more subjects. Separated out the band-power comparisons in a companion article after reviewer feedback. Source code and companion article are available at http://nicolas.brodu.numerimoire.net/en/recherche/publications
Published: 2012

38. Towards Explanatory Feedback for User Training in Brain-Computer Interfaces

Author: Julia Schumacher, Camille Jeunet, and Fabien Lotte
Subjects: medicine.diagnostic_test, InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI), Computer science, business.industry, Reliability (computer networking), Electroencephalography, Visualization, InformationSystems_MODELSANDPRINCIPLES, Human–computer interaction, medicine, Artificial intelligence, business, Mental image, Brain–computer interface
Abstract: Despite their potential for many applications, Brain -- Computer Interfaces (BCI) are still rarely used due to their low reliability and long training. These limitations are partly due to inappropriate training protocols, which includes the feedback provided to the user. While feedback should theoretically be explanatory, motivating and meaningful, current BCI feedback is usually boring, corrective only and difficult to understand. In this study, different features of the electroencephalogram signals were explored to be used as a richer, explanatory BCI feedback. First, based on offline mental imagery BCI data, muscular relaxation was notably found to be negatively correlated to BCI performance. Second, this study reports on an online BCI evaluation using muscular relaxation as additional feedback. While this additional feedback did not lead to significant change in BCI performance, this study showed that multiple feedbacks can be used without deteriorating performance and provided interesting insights for explanatory BCI feedback design.
Published: 2015

39. Exploring Large Virtual Environments by Thoughts Using a Brain–Computer Interface Based on Motor Imagery and High-Level Commands

Author: Fabrice Lamarche, Aurélien Van Langhenhove, Anatole Lécuyer, Bruno Arnaldi, Thomas Ernest, Fabien Lotte, Yann Renard, Brain-Computer Interface Laboratory - Singapore (BCI), Institute for Infocomm Research - I²R [Singapore], Perception, decision and action of real and virtual humans in virtual environments and impact on real environments (BUNRAKU), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Cachan (ENS Cachan)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Cachan (ENS Cachan)-Inria Rennes – Bretagne Atlantique
Subjects: Point of interest, Computer science, 0206 medical engineering, 02 engineering and technology, Virtual reality, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, Motor imagery, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, self-paced BCI, Computer vision, interaction technique, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], navigation, high-level commands, Brain–computer interface, SIMPLE (military communications protocol), business.industry, Interaction technique, 020601 biomedical engineering, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Human-Computer Interaction, motor imager, Task (computing), Control and Systems Engineering, virtual reality, Computer Vision and Pattern Recognition, State (computer science), Artificial intelligence, business, Brain-Computer Interface (BCI), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, Software, fuzzy inference system
Abstract: International audience; Brain-computer interfaces (BCI) are interaction devices which enable users to send commands to a computer by using brain activity only. In this paper, we propose a new interaction technique to enable users to perform complex interaction tasks and to navigate within large virtual environments (VE) by using only a BCI based on imagined movements (motor imagery). This technique enables the user to send high-level mental commands, leaving the application in charge of most of the complex and tedious details of the interaction task. More precisely, it is based on points of interest and enables subjects to send only a few commands to the application in order to navigate from one point of interest to the other. Interestingly enough, the points of interest for a given VE can be generated automatically thanks to the processing of this VE geometry. As the navigation between two points of interest is also automatic, the proposed technique can be used to navigate efficiently by thoughts within any VE. The input of this interaction technique is a newly designed self-paced BCI which enables the user to send 3 different commands based on motor imagery. This BCI is based on a fuzzy inference system with reject options. In order to evaluate the efficiency of the proposed interaction technique, we compared it with the state-of-the-art method during a task of virtual museum exploration. The state-of-the-art method uses low-level commands, which means that each mental state of the user is associated to a simple command such as turning left or moving forwards in the VE. In contrast, our method based on high-level commands enables the user to simply select its destination, leaving the application performing the necessary movements to reach this destination. Our results showed that with our interaction technique, users can navigate within a virtual museum almost twice as fast as with low-level commands, and with nearly twice less commands, meaning with less stress and more comfort for the user. This suggests that our technique enables to use efficiently the limited capacity of current motor imagery-based BCI in order to perform complex interaction tasks in VE, opening the way to promising new applications.
Published: 2010

40. Classifying EEG for brain computer interfaces using Gaussian processes

Author: Mark Girolami, Anatole Lécuyer, Mingjun Zhong, and Fabien Lotte
Subjects: Computer science, Electroencephalography, Machine learning, computer.software_genre, Kernel (linear algebra), symbols.namesake, Artificial Intelligence, medicine, Gaussian process, Brain–computer interface, Quantitative Biology::Neurons and Cognition, medicine.diagnostic_test, business.industry, Probabilistic logic, Pattern recognition, Support vector machine, ComputingMethodologies_PATTERNRECOGNITION, Kernel method, Kernel (image processing), Signal Processing, symbols, Computer Vision and Pattern Recognition, Artificial intelligence, business, computer, Software
Abstract: Classifying electroencephalography (EEG) signals is an important step for proceeding EEG-based brain computer interfaces (BCI). Currently, kernel based methods such as the support vector machine (SVM) are considered the state-of-the-art methods for this problem. In this paper, we apply Gaussian process (GP) classification to binary discrimination of motor imagery EEG data. Compared with the SVM, GP based methods naturally provide probability outputs for identifying a trusted prediction which can be used for post-processing in a BCI. Experimental results show that the classification methods based on a GP perform similarly to kernel logistic regression and probabilistic SVM in terms of predictive likelihood, but outperform SVM and K-nearest neighbor (KNN) in terms of 0-1 loss class prediction error.
Published: 2008

41. A review of classification algorithms for EEG-based brain–computer interfaces

Author: Marco Congedo, Fabrice Lamarche, Bruno Arnaldi, Fabien Lotte, Anatole Lécuyer, Perception, decision and action of real and virtual humans in virtual environments and impact on real environments (BUNRAKU), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Cachan (ENS Cachan)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), France Télécom Recherche & Développement (FT R&D), France Télécom, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Cachan (ENS Cachan)-Inria Rennes – Bretagne Atlantique
Subjects: [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Computer science, Interface (computing), Biomedical Engineering, 02 engineering and technology, Electroencephalography, Machine learning, computer.software_genre, Pattern Recognition, Automated, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Communication Aids for Disabled, User-Computer Interface, 03 medical and health sciences, Cellular and Molecular Neuroscience, InformationSystems_MODELSANDPRINCIPLES, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-BT]Computer Science [cs]/Biotechnology, Evoked Potentials, Brain–computer interface, medicine.diagnostic_test, business.industry, Brain, Classification, Statistical classification, ComputingMethodologies_PATTERNRECOGNITION, Brain-Computer Interfaces, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Algorithms, 030217 neurology & neurosurgery
Abstract: In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.
Published: 2007

42. Continuous Tactile Feedback for Motor-Imagery based Brain-Computer Interaction in a Multitasking Context

Author: Bernard N'Kaoua, Chi Thanh Vi, Sriram Subramanian, Camille Jeunet, Fabien Lotte, Daniel Spelmezan, Université de Bordeaux (UB), Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of Bristol [Bristol], Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), TC 13, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)
Subjects: Tactile Feedback, Modality (human–computer interaction), Computer science, Control (management), Context (language use), Multitasking, [SCCO]Cognitive science, Motor imagery, Human–computer interaction, Brain-Computer Interaction, Human multitasking, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Brain–computer interface
Abstract: International audience; Motor-Imagery based Brain Computer Interfaces (MI-BCIs) allow users to interact with computers by imagining limb movements. MI-BCIs are very promising for a wide range of applications as they offer a new and non-time locked modality of control. However, most MI-BCIs involve visual feedback to inform the user about the system's decisions, which makes them difficult to use when integrated with visual interactive tasks. This paper presents our design and evaluation of a tactile feedback glove for MI-BCIs, which provides a continuously updated tactile feedback. We first determined the best parameters for this tactile feedback and then tested it in a multitasking environment: at the same time users were performing the MI tasks, they were asked to count dis-tracters. Our results suggest that, as compared to an equivalent visual feedback, the use of tactile feedback leads to a higher recognition accuracy of the MI-BCI tasks and fewer errors in counting distracters.
Published: 2015

43. Classifying EEG Signals during Stereoscopic Visualization to Estimate Visual Comfort

Author: Fabien Lotte, Martin Hachet, Jérémy Frey, Aurélien Appriou, Université de Bordeaux (UB), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Popular interaction with 3d content (Potioc), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Subjects: Male, Computer science, Stereoscopy, 02 engineering and technology, Electroencephalography, law.invention, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], User-Computer Interface, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, law, Surveys and Questionnaires, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Signal processing, Brain Mapping, medicine.diagnostic_test, General Neuroscience, Brain, General Medicine, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Stereopsis, Brain-Computer Interfaces, lcsh:R858-859.7, 020201 artificial intelligence & image processing, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Female, Monte Carlo Method, Research Article, General Computer Science, Article Subject, General Mathematics, lcsh:Computer applications to medicine. Medical informatics, Statistics, Nonparametric, lcsh:RC321-571, 03 medical and health sciences, Young Adult, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Adaptive system, Sensation, medicine, Humans, Computer Simulation, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Simulation, Brain–computer interface, Depth Perception, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, Brain Waves, Artificial intelligence, Depth perception, business, 030217 neurology & neurosurgery, Photic Stimulation
Abstract: With stereoscopic displays a sensation of depth that is too strong could impede visual comfort and may result in fatigue or pain. We used Electroencephalography (EEG) to develop a novel brain-computer interface that monitors users’ states in order to reduce visual strain. We present the first system that discriminates comfortable conditions from uncomfortable ones during stereoscopic vision using EEG. In particular, we show that either changes in event-related potentials’ (ERPs) amplitudes or changes in EEG oscillations power following stereoscopic objects presentation can be used to estimate visual comfort. Our system reacts within 1 s to depth variations, achieving 63% accuracy on average (up to 76%) and 74% on average when 7 consecutive variations are measured (up to 93%). Performances are stable (≈62.5%) when a simplified signal processing is used to simulate online analyses or when the number of EEG channels is lessened. This study could lead to adaptive systems that automatically suit stereoscopic displays to users and viewing conditions. For example, it could be possible to match the stereoscopic effect with users’ state by modifying the overlap of left and right images according to the classifier output.
Published: 2015

44. User-Centred BCI Videogame Design

Author: Fabien Lotte, Guillaume Loup, Emilie Loup-Escande, Anatole Lécuyer, Université de Picardie Jules Verne (UPJV), Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Laboratoire d'Informatique de l'Université du Mans (LIUM), Le Mans Université (UM), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-MEDIA ET INTERACTIONS (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), R. Nakatsu and M. Rauterberg and P. Ciancarini, Centre de Recherche en Psychologie : Cognition, Psychisme et Organisations - UR UPJV 7273 (CRP-CPO), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)
Subjects: Multimedia, business.industry, Computer science, 05 social sciences, Human factors and ergonomics, Usability, computer.software_genre, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], 03 medical and health sciences, 0302 clinical medicine, User experience design, Human–computer interaction, [SCCO.PSYC]Cognitive science/Psychology, Design process, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], business, User needs, computer, 050107 human factors, 030217 neurology & neurosurgery, Brain–computer interface
Abstract: International audience; This chapter aims to offer a user-centred methodological framework to guide the design and evaluation of Brain-Computer Interface videogames. This framework is based on the contributions of ergonomics to ensure these games are well suited for their users (i.e., players). It provides methods, criteria and metrics to complete the different phases required by ae human-centred design process. This aims to understand the context of use, specify the user needs and evaluate the solutions in order to define design choices. Several ergonomic methods (e.g., interviews, longitudinal studies, user based testing), objective metrics (e.g., task success, number of errors) and subjective metrics (e.g., mark assigned to an item) are suggested to define and measure the usefulness, usability, acceptability, hedonic qualities, appealingness, emotions related to user experience, immersion and presence to be respected. The benefits and contributions of the user centred framework for the ergonomic design of these Brain-Computer Interface Videogames are discussed.
Published: 2015

45. Electroencephalography (EEG)-based Brain-Computer Interfaces

Author: Fabien Lotte, Laurent Bougrain, Maureen Clerc, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Popular interaction with 3d content (Potioc), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre National de la Recherche Scientifique (CNRS), Université de Lorraine (UL), Analysis and modeling of neural systems by a system neuroscience approach (NEUROSYS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: Brain activity and meditation, Computer science, 0206 medical engineering, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Electroencephalography, Field (computer science), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, Software, InformationSystems_MODELSANDPRINCIPLES, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Human–computer interaction, medicine, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-BT]Computer Science [cs]/Biotechnology, Brain–computer interface, Signal processing, Focus (computing), medicine.diagnostic_test, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, 020601 biomedical engineering, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], business, 030217 neurology & neurosurgery
Abstract: International audience; Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). In this article, we aim at providing an accessible and up-to-date overview of EEG-based BCI, with a main focus on its engineering aspects. We notably introduce some basic neuroscience background, and explain how to design an EEG-based BCI, in particular reviewing which signal processing, machine learning, software and hardware tools to use. We present Brain Computer Interface applications, highlight some limitations of current systems and suggest some perspectives for the field.
Published: 2015

46. Call For Book Chapters: Brain-Computer Interfaces Handbook: Technological and Theoretical Advances

Author: Anton Nijholt, Chang Nam, and Fabien Lotte
Subjects: Human-Computer Interaction, Behavioral Neuroscience, Interfacing, Computer science, Human–computer interaction, Biomedical Engineering, Key (cryptography), Electrical and Electronic Engineering, Engineering physics, Brain–computer interface
Abstract: This handbook provides a synopsis of key findings and technological and theoretical advances directly applicable to brain-computer interfacing (BCI) technologies, readily understood and applied by ...
Published: 2016

47. EEG-based workload estimation across affective contexts

Author: Fabien Lotte, Christian Mühl, Camille Jeunet, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Passive Brain Computer Interface, Computer science, Context (language use), Machine learning, computer.software_genre, 050105 experimental psychology, Task (project management), lcsh:RC321-571, workload, 03 medical and health sciences, brain-computer interfaces, stress, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Robustness (computer science), 0501 psychology and cognitive sciences, Original Research Article, Resilience (network), Affective computing, affective computing, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Brain–computer interface, business.industry, General Neuroscience, [SCCO.NEUR]Cognitive science/Neuroscience, brain–computer interface, 05 social sciences, Workload, Electroencephalography, Classification, Feature (computer vision), Brain-computer interface, [SCCO.PSYC]Cognitive science/Psychology, Flugphysiologie, Artificial intelligence, Data mining, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, physiological computing, Neuroscience
Abstract: International audience; Workload estimation from electroencephalographic signals (EEG) offers a highly sensitive tool to adapt the human-computer interaction to the user state. To create systems that reliably work in the complexity of the real world, a robustness against contextual changes (e.g., mood), has to be achieved. To study the resilience of state-of-the-art EEG-based workload classification against stress we devise a novel experimental protocol, in which we manipulated the affective context (stressful/non-stressful) while the participant solved a task with two workload levels. We recorded self-ratings, behavior, and physiology from 24 participants to validate the protocol. We test the capability of different, subject-specific workload classifiers using either frequency-domain, time-domain, or both feature varieties to generalize across contexts. We show that the classifiers are able to transfer between affective contexts, though performance suffers independent of the used feature domain. However, cross-context training is a simple and powerful remedy allowing the extraction of features in all studied feature varieties that are more resilient to task-unrelated variations in signal characteristics. Especially for frequency-domain features, across-context training is leading to a performance comparable to within-context training and testing. We discuss the significance of the result for neurophysiology-based workload detection in particular and for the construction of reliable passive brain-computer interfaces in general.
Published: 2014

48. A Tutorial on EEG Signal-processing Techniques for Mental-state Recognition in Brain–Computer Interfaces

Author: Fabien Lotte
Subjects: Signal processing, Class (computer programming), Statistical classification, Motor imagery, medicine.diagnostic_test, Computer science, Speech recognition, Key (cryptography), medicine, Electroencephalography, Linear discriminant analysis, Brain–computer interface
Abstract: This chapter presents an introductory overview and a tutorial of signal-processing techniques that can be used to recognize mental states from electroencephalographic (EEG) signals in brain–computer interfaces. More particularly, this chapter presents how to extract relevant and robust spectral, spatial, and temporal information from noisy EEG signals (e.g., band-power features, spatial filters such as common spatial patterns or xDAWN, etc.), as well as a few classification algorithms (e.g., linear discriminant analysis) used to classify this information into a class of mental state. It also briefly touches on alternative, but currently less used approaches. The overall objective of this chapter is to provide the reader with practical knowledge about how to analyze EEG signals as well as to stress the key points to understand when performing such an analysis.
Published: 2014

49. Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design

Author: Fabien Lotte, Christian Mühl, Florian Larrue, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Popular interaction with 3d content (Potioc), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Subjects: [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Brain-Computer Interface, Computer science, [SHS.EDU]Humanities and Social Sciences/Education, Control (management), [SCCO.COMP]Cognitive science/Computer science, training protocols, instructional design, feedback, Training approach, 050105 experimental psychology, lcsh:RC321-571, 03 medical and health sciences, Behavioral Neuroscience, 0302 clinical medicine, InformationSystems_MODELSANDPRINCIPLES, motivation, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Robustness (computer science), Human–computer interaction, Human-in-the-loop, 0501 psychology and cognitive sciences, EEG, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], BCI, [INFO.INFO-BT]Computer Science [cs]/Biotechnology, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Biological Psychiatry, Brain–computer interface, Signal processing, business.industry, Instructional design, 05 social sciences, brain-computer interface (BCI), Hypothesis and Theory Article, Psychiatry and Mental health, Neuropsychology and Physiological Psychology, Neurology, Brain-Computer Interfaces, [SCCO.PSYC]Cognitive science/Psychology, Key (cryptography), [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, Artificial intelligence, business, 030217 neurology & neurosurgery, electroencephalography, Mental image, Neuroscience
Abstract: International audience; While recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI) often rely on mutual learning efforts by the user and the machine, with BCI users learning to produce stable ElectroEncephaloGraphy (EEG) patterns (spontaneous BCI control being widely acknowledged as a skill) while the computer learns to automatically recognize these EEG patterns, using signal processing. Most research so far was focused on signal processing, mostly neglecting the human in the loop. However, how well the user masters the BCI skill is also a key element explaining BCI robustness. Indeed, if the user is not able to produce stable and distinct EEG patterns, then no signal processing algorithm would be able to recognize them. Unfortunately, despite the importance of BCI training protocols, they have been scarcely studied so far, and used mostly unchanged for years. In this paper, we advocate that current human training approaches for spontaneous BCI are most likely inappropriate. We notably study instructional design literature in order to identify the key requirements and guidelines for a successful training procedure that promotes a good and efficient skill learning. This literature study highlights that current spontaneous BCI user training procedures satisfy very few of these requirements and hence are likely to be suboptimal. We therefore identify the flaws in BCI training protocols according to instructional design principles, at several levels: in the instructions provided to the user, in the tasks he/she has to perform, and in the feedback provided. For each level, we propose new research directions that are theoretically expected to address some of these flaws and to help users learn the BCI skill more efficiently.
Published: 2013

50. Combining BCI with Virtual Reality: Towards New Applications and Improved BCI

Author: Josef Faller, Robert Leeb, Gert Pfurtscheller, Yann Renard, Christoph Guger, Fabien Lotte, Anatole Lécuyer, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Popular interaction with 3d content (Potioc), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Graz University of Technology [Graz] (TU Graz), g.tec medical engineering [Autriche], g.tec medical engineering, Brain-Computer interface (BCI), Brain-Computer interface, Laboratory of Brain-Computer Interfaces (GRAZ BCI), Graz University of Technology [Graz] (TU Graz)-Institute for Knowledge Discovery, 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-MEDIA ET INTERACTIONS (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Ecole Polytechnique Fédérale de Lausanne (EPFL), Allison, Brendan Z. and Dunne, Stephen and Leeb, Robert and Millán, José Del R. and Nijholt, Anton, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Allison, Brendan, Dunne, Stephen, Leeb, Robert, Millán, José del R., Nijholt, Anton, Lotte, Fabien, and Allison, Brendan Z. and Dunne, Stephen and Leeb, Robert and Millán, José Del R. and Nijholt, Anton
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Engineering, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Brain activity and meditation, 0206 medical engineering, [INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR], 02 engineering and technology, Virtual reality, Communications system, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, Motor imagery, Home automation, Human–computer interaction, [INFO.INFO-BT]Computer Science [cs]/Biotechnology, Brain–computer interface, business.industry, 020601 biomedical engineering, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], [SDV.BIO] Life Sciences [q-bio]/Biotechnology, [INFO.INFO-BT] Computer Science [cs]/Biotechnology, Virtual machine, business, computer, 030217 neurology & neurosurgery
Abstract: International audience; Brain-Computer Interfaces (BCI) are communication systems which can convey messages through brain activity alone. Recently BCIs were gaining interest among the virtual reality (VR) community since they have appeared as promising interaction devices for virtual environments (VEs). Especially these implicit interaction techniques are of great interest for the VR community, e.g., you are imaging the movement of your hand and the virtual hand is moving, or you can navigate through houses or museums by your thoughts alone or just by looking at some highlighted objects. Furthermore, VE can provide an excellent testing ground for procedures that could be adapted to real world scenarios, especially patients with disabilities can learn to control their movements or perform specific tasks in a VE. Several studies will highlight these interactions.
Published: 2013

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