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16 results on '"Popular interaction with 3d content (Potioc)"'

1. Exploring How Visualization Design and Situatedness Evoke Compassion in the Wild

Author

L. Morais, N. Andrade, D. Sousa, Luiz, Morais, Visualisations situées pour l'analyse de données personnelles - - EMBER2019 - ANR-19-CE33-0012 - AAPG2019 - VALID, 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), Universidade Federal de Campina Grande [Campina Grande] (UFCG), and ANR-19-CE33-0012,EMBER,Visualisations situées pour l'analyse de données personnelles(2019)

Subjects
[INFO.INFO-CY] Computer Science [cs]/Computers and Society [cs.CY], [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], Computer Graphics and Computer-Aided Design
Abstract

International audience; This work explores how the design and situatedness of data representations affect people’s compassion with a case study concerning harassment episodes in a public place. Results contribute to advancing the understanding of how visualizations can evoke emotions and their impact on prosocial behaviors, such as helping people in need. Recent literature examined the effect of different on-screen data representations on emotion or prosociality, but little has been done concerning visualizations shown in a public place — especially a space contextually relevant to the data — or presented through unconventional media formats such as physical marks. We conducted two in-the-wild studies to investigate how different factors affect people’s self-reported compassion and intention to donate. We compared three ways of presenting data about the harassment cases: (1) communicating data only verbally; (2) using a printed poster with aggregated information; and (3) using a physicalization with detailed information about each story. We found that the physicalization influenced people to donate more than only hearing about the data, but it is unclear if the same applied to the poster visualization. Also, passers-by reported a likely small increase in compassion when they saw the physicalization instead of the poster. We also examined the role of situatedness by showing the physicalization in a site that is not contextually relevant to the data. Our results suggest that people had a similar intention to donate and levels of compassion in both places. Those findings may indicate that using specific visualization designs to support campaigns about sensitive causes (e.g., sexual harassment) can increase the emotional response of passers-by and may motivate them to help, independently of where the data representation is shown. Finally, this work also informs on the strengths and weaknesses of using research in the wild to evaluate data visualizations in public spaces.

Published
2022
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2. X-Road

Author

Lauren Thévin, Carine Briant, Anke Brock, Ludwig Maximilian University [Munich] (LMU), 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), Institution Régionale des Sourds et des Aveugles (IRSA), ENAC - Equipe Informatique Interactive (LII), Ecole Nationale de l'Aviation Civile (ENAC), This project was conducted with the support of the Erasmus+ Program of the European Union Pr. no 2016-1-EL01-KA201-023731., 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, Visual impairment, Augmented reality, 02 engineering and technology, Space (commercial competition), Virtual reality, Training (civil), Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, medicine, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], 050107 human factors, Bespoke, 05 social sciences, Orientation and Mobility, 020207 software engineering, Accessibility, Computer Science Applications, Human-Computer Interaction, Mobility training, Audio feedback, medicine.symptom
Abstract

International audience; Orientation and Mobility (O&M) classes teach people with visual impairments how to navigate the world, for instance how to cross a road. Yet, this training can be difficult and dangerous due to conditions such as traffic and weather. Virtual Reality (VR) can overcome these challenges by providing interactive controlled environments. However, most existing VR tools rely on visual feedback, which limits their use with students with visual impairment. In a collaborative design approach with O&M instructors, we designed an affordable and accessible VR system for O&M classes, called X-Road. Using a smartphone and a bespoke headmount, X-Road provides both visual and audio feedback and allows users to move in space, as in the real world. In a study with 13 students with visual impairments, X-Road proved to be an effective alternative to teaching and learning classical O&M tasks, and both students and instructors were enthusiastic about this technology. We conclude with design recommendations for inclusive VR systems.

Published
2020
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3. 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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4. Publishing Visualization Studies as Registered Reports: Expected Benefits and Researchers' Attitudes

Author

Yvonne Jansen, Petra Isenberg, Pierre Dragicevic, Anastasia Bezerianos, Lonni Besançon, Monash University [Melbourne], Linköpings universitet, Interacting with Large Data (ILDA), 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)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Analysis and Visualization (AVIZ), 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), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
Medical education, Publishing, business.industry, Research quality, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], business, Psychology, Visualization
Abstract

Background: Registered Reports are publications in which study proposals are peer reviewed and pre-accepted before the study is ran. Their adoption in other disciplines has been found to promote research quality and save time and resources. Objectives: We offer a brief introduction to Registered Reports and their expected benefits for visualization research. We then report a survey of the visualization community on their attitudes towards Registered Reports. This survey takes the form of a quasi Registered Report, the first one presented at a visualization venue as far as we know.Method: We will run an online survey that will be open from the end of August until the day of the alt.VIS workshop.Results: Most respondents expressed interest in Registered Reports as an additional submission format. Junior researchers as well as researchers with prior experience of preregistration tended to be more inclined to adopt this format. However, qualitative feedback highlights several perceived drawbacks and worries about Registered Reports, such as low suitability for some types of research, and increased workload for reviewers and authors.Conclusion: While many respondents expressed interest in (and sometimes enthusiasm for) Registered Reports, many also voiced concerns that should be considered and discussed in the community.Reproducibility: All the material is available on osf.io/4nrma/.

Published
2021

5. 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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6. A physical learning companion for Mental-Imagery BCI User Training

Author

Bernard N'Kaoua, Roger Nkambou, Léa Pillette, Boris Mansencal, 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), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), 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), Laboratoire de recherche en Gestion, Diffusion et Acquisition des Connaissances (Laboratoire GDAC), Université du Québec à Montréal = University of Québec in Montréal (UQAM), 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), This work was supported by the French National Research Agency (project REBEL, grant ANR-15-CE23-0013-01), the European Research Council with the Brain-Conquest project (grant ERC-2016-STG-714567) and the Initiative of Excellence (IdEx) from the University of Bordeaux, France, 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)-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), 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
Social feedback, Computer science, Learning Companion, Psychological intervention, Human Factors and Ergonomics, Context (language use), Memorization, Education, Task (project management), [SCCO]Cognitive science, 03 medical and health sciences, 0302 clinical medicine, User experience design, Human–computer interaction, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Mental-Imagery based Brain-Computer Interface, 030304 developmental biology, 0303 health sciences, Facial expression, User experience, business.industry, Emotional feedback, General Engineering, Human-Computer Interaction, Hardware and Architecture, business, 030217 neurology & neurosurgery, Software, Mental image
Abstract

International audience; Mental-Imagery based Brain-Computer Interfaces (MI-BCI) present new opportunities to interact with digital technologies, such as wheelchairs or neuroprostheses, only by performing mental imagery tasks (e.g., imagining an object rotating or imagining hand movements). MI-BCIs can also be used for several applications such as communication or post-stroke rehabilitation. Though, their lack of reliability remains a barrier to a larger scale development of the technology. For example, one task between two is recognized on average 75% of the time. It has been shown that users are more likely to struggle using MI-BCIs if they are non-autonomous or tensed. This might, at least in part, result from a lack of social presence and emotional support, which have yet very little been tested in MI-BCI, despite recommendations from the educational literature. One way to provide such social and emotional context is by using a learning companion. Therefore, we designed, implemented and evaluated the first learning companion dedicated to the improvement of MI-BCI user training. We called this companion PEANUT for Personalized Emotional Agent for Neurotechnology User Training. PEANUT provided social presence and emotional support, depending on the performance and progress of the user, through interventions combining both pronounced sentences and facial expressions. It was designed based on the literature, data analyses and user-studies. We notably conducted various online user surveys to identify the desired characteristics of our learning companion in terms of appearance and supporting speech content. From the results of these surveys we notably deduced which should be the characteristics (personal/non-personal, exclamatory/declarative) of the sentences to be used depending on the performance and progression of a learner. We also found that eyebrows could increase expressiveness of cartoon-like faces. Then, once this companion was implemented, we evaluated it during real online MI-BCI use. We found that non-autonomous people, i.e., who are more inclined to work in a group, that are usually disadvantaged when using MI-BCI were advantaged compared to autonomous people when PEANUT was present with an increase of 3.9% of peak performances. Furthermore, in terms of user experience, PEANUT seems to have improved how people felt about their ability to learn and memorize how to use an MI-BCI by 7.4%, which is a dimension of the user experience we assessed.

Published
2020
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7. EEG-based neuroergonomics for 3d user interfaces: opportunities and challenges

Author

Fabien Lotte, Jérémy Frey, Martin Hachet, 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)-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 Lotte, Fabien

Subjects
media_common.quotation_subject, Human Factors and Ergonomics, 02 engineering and technology, [SCCO]Cognitive science, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Human–computer interaction, [INFO.INFO-ET] Computer Science [cs]/Emerging Technologies [cs.ET], 0202 electrical engineering, electronic engineering, information engineering, Neuroergonomics, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-BT]Computer Science [cs]/Biotechnology, 050107 human factors, General Psychology, Applied Psychology, media_common, [INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM], 05 social sciences, [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], 020207 software engineering, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [SCCO] Cognitive science, Art, [INFO.INFO-BT] Computer Science [cs]/Biotechnology, Industrial relations, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], Humanities
Abstract

Les interfaces utilisateurs 3D sont utilisees dans un nombre croissant d’applications, qui vont du divertissement au design industriel. Cependant, les tâches d’interaction 3D sont en general plus complexes pour les utilisateurs etant-donne qu’il est plus demandeur au niveau cognitif d’interagir avec un environnement en 3D que de percevoir et d’interagir avec un environnement en 2D. Ainsi, il est essentiel que nous puissions finement evaluer l’experience utilisateur, afin de proposer des interfaces faciles d’utilisation. Pour ce faire, une direction de recherche prometteuse est de mesurer l’etat interne des utilisateurs via l’acquisition de signaux cerebraux pendant l’interaction, en suivant une approche neuro­ergonomique. Combine a des methodes existantes, un tel outil peut etre utilise pour ameliorer la comprehension de l’experience utilisateur. Dans cet article, nous passons en revue le travail qui est mene dans ce domaine ; ce qui a deja ete accompli et les challenges qui se font jour. Nous decrivons comment une technique d’imagerie cerebrale mobile telle que l’electroencephalographie (EEG) permet des mesures continues qui ne perturbent pas l’interaction. Une evaluation des utilisateurs centree sur l’EEG peut donner un apercu de multiples dimensions de l’experience utilisateur, avec des taches d’interaction realistes ou des interfaces novatrices. Nous etudions quatre construits : la charge mentale, l’attention, la reconnaissance d’erreur et le confort visuel. A terme, ces metriques pourraient aider a soulager les utilisateurs lorsqu’ils interagissent avec des ordinateurs.

Published
2017
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8. Inria: From Cold-War Computing to Digital Sciences

Author

François X. Sillion, Philippe Nain, Pascal Guitton, 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), Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-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 Grenoble Alpes [2016-2019] (UGA [2016-2019])-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Inria Siège, 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)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Institut Rhône-Alpin des systèmes complexes (IXXI)

Subjects
history of computing, General Computer Science, Computer science, Cold War, Cold war, 0202 electrical engineering, electronic engineering, information engineering, Art history, [INFO]Computer Science [cs], 020207 software engineering, Inria, 02 engineering and technology
Abstract

International audience; Inria, the French institute for research in digital sciences, celebrates its 50th anniversary this year. The authors reflect on Inria's evolution through the decades and present its vision for the future.

Published
2017
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9. Spatial Interfaces and Interactive 3D Environments for Immersive Musical Performances

Author

Florent Berthaut, Martin Hachet, Méthodes et outils pour l'Interaction à gestes (MINT2), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-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)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), 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
Computer science, media_common.quotation_subject, Emotions, Interactive 3d, Entertainment industry, Musical, Virtual reality, computer.software_genre, 050105 experimental psychology, 060404 music, Creativity, Computer graphics, Imaging, Three-Dimensional, Human–computer interaction, Immersion (virtual reality), Humans, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Graphics, Set (psychology), media_common, Multimedia, 05 social sciences, 06 humanities and the arts, Computer Graphics and Computer-Aided Design, computer, Music, 0604 arts, Software
Abstract

International audience; The power of interactive 3D graphics, immersive displays, and spatial interfaces is still under-explored in domains where the main target is to enhance creativity and emotional experiences. This article presents a set of work the attempts to extent the frontiers of music creation as well as the experience of audiences attending to digital performances. The goal is to connect sounds to interactive 3D graphics that musicians can interact with and the audience can observe.

Published
2016
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10. 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
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11. 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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12. 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
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13. Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns

Author

Jeunet, Camille, N'Kaoua, Bernard, Subramanian, Sriram, Hachet, Martin, Lotte, Fabien, 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), Programming Language Technology For Communication Services (Phoenix), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Handicap et système nerveux :Action, communication, interaction: rétablissement de la fonction et de la participation [Bordeaux] (EA4136), UFR Sciences médicales 3 [Bordeaux]-Université de Bordeaux Ségalen [Bordeaux 2]-UFR Sciences médicales 3 [Bordeaux]-Université de Bordeaux Ségalen [Bordeaux 2], Department of Informatics [Sussex], University of Sussex, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), IdEx BordeauxBCI - Lift, 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, Handicap et système nerveux :Action, communication, interaction: rétablissement de la fonction et de la participation [Bordeaux] (EA4136), UFR Sciences médicales 3 [Bordeaux]-Université de Bordeaux Ségalen [Bordeaux 2]-UFR Sciences médicales 3 [Bordeaux]-Université de Bordeaux Ségalen [Bordeaux 2]-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 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), 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, Imagery, Psychotherapy, lcsh:R, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Neurophysiology, Reproducibility of Results, lcsh:Medicine, Electroencephalography, Personality Disorders, Young Adult, Cognition, Brain-Computer Interfaces, QZ, Humans, Learning, Female, lcsh:Q, lcsh:Science, Personality, Research Article
Abstract

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands\ud to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy—\ud EEG), which is processed while they perform specific mental tasks. While very\ud promising, MI-BCIs remain barely used outside laboratories because of the difficulty\ud encountered by users to control them. Indeed, although some users obtain good control\ud performances after training, a substantial proportion remains unable to reliably control an\ud MI-BCI. This huge variability in user-performance led the community to look for predictors of\ud MI-BCI control ability. However, these predictors were only explored for motor-imagery\ud based BCIs, and mostly for a single training session per subject. In this study, 18 participants\ud were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2\ud of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships\ud between the participants’ BCI control performances and their personality, cognitive\ud profile and neurophysiological markers were explored. While no relevant relationships with\ud neurophysiological markers were found, strong correlations between MI-BCI performances\ud and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive\ud model of MI-BCI performance based on psychometric questionnaire scores was proposed.\ud A leave-one-subject-out cross validation process revealed the stability and reliability of this\ud model: it enabled to predict participants’ performance with a mean error of less than 3\ud points. This study determined how users’ profiles impact their MI-BCI control ability and\ud thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of\ud each user.

Published
2015
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14. 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
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15. Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems

Author

Andreas Meinel, Sebastián Castaño-Candamil, Benjamin Blankertz, Fabien Lotte, Michael Tangermann, Lotte, Fabien, Albert-Ludwigs-Universität Freiburg, Technical University of Berlin / Technische Universität Berlin (TU), 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), Technische Universität Berlin (TU), 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
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-BT] Computer Science [cs]/Biotechnology, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-BT]Computer Science [cs]/Biotechnology, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

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16. When should MI-BCI feature optimization include prior knowledge, and which one?

Author

Camille Benaroch, Maria Sayu Yamamoto, Aline Roc, Pauline Dreyer, Camille Jeunet, Fabien Lotte, 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), Cognition, Langues, Langage, Ergonomie (CLLE), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J), 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), Université de Toulouse (UT), Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS)

Subjects
Human-Computer Interaction, Behavioral Neuroscience, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Biomedical Engineering, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-BT]Computer Science [cs]/Biotechnology, Electrical and Electronic Engineering, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Motor imagery-based brain-computer interfaces (MI-BCIs) rely on interactions between humans and machines. Therefore, the (learning) characteristics of both components are key to understand and improve performances. Data-driven methods are often used to select/extract features with very little neurophysiological prior. Should such approach include prior knowledge and, if so, which one? This paper studies the relationship between BCI performances and characteristics of the subject-specific Most Discriminant Frequency Band (MDFB) selected by a popular heuristic algorithm. First, our results showed a correlation between the selected MDFB characteristics (mean and width) and performances. Then, to investigate a possible causality link, we compared, online, performances obtained with a constrained (enforcing characteristics associated to high performances) and an unconstrained algorithm. Although we could not conclude on causality, average performances using the constrained algorithm were the highest. Finally, to understand the relationship between MDFB characteristics and performances better, we used machine learning to 1) predict MI-BCI performances using MDFB characteristics and 2) select automatically the optimal algorithm (constrained or unconstrained) for each subject. Our results revealed that the constrained algorithm could improve performances for subjects with either clearly distinct or no distinct EEG patterns.

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