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Start Over Author "Popular interaction with 3d content (Potioc)" Journal journal of neural engineering
4 results on '"Popular interaction with 3d content (Potioc)"'

1. Standardization of protocol design for user training in EEG-based brain-computer interface

Author

Jelena Mladenović, 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), Mladenovic, Jelena, 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
Standardization, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI), Computer science, Interface (computing), media_common.quotation_subject, Biomedical Engineering, [SCCO.COMP]Cognitive science/Computer science, Context (language use), 050105 experimental psychology, 03 medical and health sciences, Cellular and Molecular Neuroscience, User-Computer Interface, InformationSystems_MODELSANDPRINCIPLES, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [SCCO.COMP] Cognitive science/Computer science, Perceptual affordance, Human–computer interaction, Perception, Human-computer interaction (HCI), Standadization, Humans, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Electroencephalography (EEG), Affordance, User training, Brain–computer interface, media_common, Haptic technology, Brain-computer interfaces (BCI), SIMPLE (military communications protocol), 05 social sciences, Electroencephalography, Reference Standards, Protocal design, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Brain-Computer Interfaces, [SCCO.PSYC] Cognitive science/Psychology, [SCCO.PSYC]Cognitive science/Psychology, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], 030217 neurology & neurosurgery
Abstract

Brain-computer interfaces (BCIs) are systems that enable a person to interact with a machine using only neural activity. Such interaction can be non-intuitive for the user hence user trainings are developed to increase one's understanding, confidence and motivation, which would in parallel increase system performance. To clearly address the current issues in the BCI user training protocol design, here it is divided into introductory period and BCI interaction period. First, the introductory period (before BCI interaction) must be considered as equally important as the BCI interaction for user training. To support this claim, a review of papers show that BCI performance can depend on the methodologies presented in such introductory period. To standardize its design, the literature from human-computer interaction (HCI) is adjusted to the BCI context. Second, during the user-BCI interaction, the interface can take a large spectrum of forms (2D, 3D, size, color etc.) and modalities (visual, auditory or haptic etc.) without following any design standard or guidelines. Namely, studies that explore perceptual affordance on neural activity, show that motor neurons can be triggered from a simple observation of certain objects, and depending on objects' properties (size, location etc.) neural reactions can vary greatly. Surprisingly, the effects of perceptual affordance were not investigated in the BCI context. Both inconsistent introductions to BCI as well as variable interface designs make it difficult to reproduce experiments, predict their outcomes and compare results between them. To address these issues, a protocol design standardization for user training is proposed.

Published
2020

2. 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
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3. 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
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4. 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

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