7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Related collections

          Most cited references49

          • Record: found
          • Abstract: not found
          • Article: not found

          Technology Acceptance Model 3 and a Research Agenda on Interventions

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            A well-conditioned estimator for large-dimensional covariance matrices

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

              Most current electroencephalography (EEG)-based brain-computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                18 March 2021
                2021
                : 15
                : 635653
                Affiliations
                [1] 1Inria Bordeaux Sud-Ouest , Talence, France
                [2] 2LaBRI (CNRS, Univ. Bordeaux, Bordeaux INP) , Talence, France
                [3] 3CLLE Lab, CNRS, Univ. Toulouse Jean Jaurès , Toulouse, France
                Author notes

                Edited by: Bin He, Carnegie Mellon University, United States

                Reviewed by: Dongrui Wu, Huazhong University of Science and Technology, China; Serafeim Perdikis, University of Essex, United Kingdom

                *Correspondence: Fabien Lotte fabien.lotte@ 123456inria.fr

                This article was submitted to Brain-Computer Interfaces, a section of the journal Frontiers in Human Neuroscience

                †These authors have contributed equally to this work

                Article
                10.3389/fnhum.2021.635653
                8012558
                33815081
                dc3af3a8-8ee3-4316-b8b5-51997d3c1dc0
                Copyright © 2021 Benaroch, Sadatnejad, Roc, Appriou, Monseigne, Pramij, Mladenovic, Pillette, Jeunet and Lotte.

                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.

                History
                : 30 November 2020
                : 18 February 2021
                Page count
                Figures: 13, Tables: 0, Equations: 15, References: 50, Pages: 22, Words: 14360
                Categories
                Human Neuroscience
                Original Research

                Neurosciences
                brain computer interface,user training,electroencaphlography,riemannian classification,tetraplegic or quadriplegic people,adaptive classification,user experience,learning metrics

                Comments

                Comment on this article