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Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training

While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that...

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Autores principales: Benaroch, Camille, Sadatnejad, Khadijeh, Roc, Aline, Appriou, Aurélien, Monseigne, Thibaut, Pramij, Smeety, Mladenovic, Jelena, Pillette, Léa, Jeunet, Camille, Lotte, Fabien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012558/
https://www.ncbi.nlm.nih.gov/pubmed/33815081
http://dx.doi.org/10.3389/fnhum.2021.635653
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author Benaroch, Camille
Sadatnejad, Khadijeh
Roc, Aline
Appriou, Aurélien
Monseigne, Thibaut
Pramij, Smeety
Mladenovic, Jelena
Pillette, Léa
Jeunet, Camille
Lotte, Fabien
author_facet Benaroch, Camille
Sadatnejad, Khadijeh
Roc, Aline
Appriou, Aurélien
Monseigne, Thibaut
Pramij, Smeety
Mladenovic, Jelena
Pillette, Léa
Jeunet, Camille
Lotte, Fabien
author_sort Benaroch, Camille
collection PubMed
description While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. In this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCI by performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvement was effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user's neurophysiological patterns and user experience throughout the BCI training and competition.
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spelling pubmed-80125582021-04-02 Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training Benaroch, Camille Sadatnejad, Khadijeh Roc, Aline Appriou, Aurélien Monseigne, Thibaut Pramij, Smeety Mladenovic, Jelena Pillette, Léa Jeunet, Camille Lotte, Fabien Front Hum Neurosci Human Neuroscience While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. In this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCI by performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvement was effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user's neurophysiological patterns and user experience throughout the BCI training and competition. Frontiers Media S.A. 2021-03-18 /pmc/articles/PMC8012558/ /pubmed/33815081 http://dx.doi.org/10.3389/fnhum.2021.635653 Text en Copyright © 2021 Benaroch, Sadatnejad, Roc, Appriou, Monseigne, Pramij, Mladenovic, Pillette, Jeunet and Lotte. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Human Neuroscience
Benaroch, Camille
Sadatnejad, Khadijeh
Roc, Aline
Appriou, Aurélien
Monseigne, Thibaut
Pramij, Smeety
Mladenovic, Jelena
Pillette, Léa
Jeunet, Camille
Lotte, Fabien
Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training
title Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training
title_full Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training
title_fullStr Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training
title_full_unstemmed Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training
title_short Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training
title_sort long-term bci training of a tetraplegic user: adaptive riemannian classifiers and user training
topic Human Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012558/
https://www.ncbi.nlm.nih.gov/pubmed/33815081
http://dx.doi.org/10.3389/fnhum.2021.635653
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