The CYBATHLON BCI series 2019

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. Therefore we designed and studied 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.

This encouraged us to tackle multiple challenges associated with BCI use over multiple days, in ``real life", with an actual end-user. Challenges were related to non-stationarity problems, user training but also managing the short time we had before the competition.

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. Our results showed a learning at all levels (i.e., user, machine, and experimenters). Indeed, during the few months of training we were able to observe a user learning. At the end of the competition, we were ranked 5th out of 6

Game with the mental tasks .

NITRO 1 et 2 teams.

Publications in scientific journals

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

C. Benaroch, K. Sadatnejad, A. Roc, A. Appriou, T. Monseigne, S. Pramij, J. Mladenovic, L. Pillette, C. Jeunet, F. Lotte

Frontiers in Human Neuroscience, 2021