Apprendre à contrôler une interface cerveau-ordinateur : le projet BrainConquest

International audienceLes interfaces cerveau-ordinateur (ou Brain-Computer Interface – BCI) sont des neurotechnologies très prometteuses pour de nombreuses applications. Mais elles sont actuellement encore insuffisamment fiables. Les rendre fiables et utilisables nécessite non seulement des améliorations côté machine (par exemple, en améliorant leurs algorithmes d’analyse des signaux cérébraux), mais aussi côté utilisateur. En effet, contrôler une BCI est une compétence qui s’apprend et qui demande de la pratique. Malheureusement, la communauté scientifique comprend encore très mal comment entraîner cette compétence efficacement. Dans cet article, nous présentons les recherches menées dans le cadre du projet BrainConquest, dont l’objectif est justement de comprendre, de modéliser et d’optimiser cet entraînement utilisateur dans les BCI. Nous illustrons ainsi au travers d’exemples les différents facteurs qui peuvent influencer les performances de contrôle d’une BCI (par exemple, la personnalité de l’utilisateur, ou son état mental), le type de retour perceptif (le feedback) et le type d’exercices d’entraînement qui peuvent être proposés aux utilisateurs, ou encore les applications concrètes de ces entraînements BCI, par exemple des technologies d’assistance ou en matière de rééducation motrice

Are users' traits informative enough to predict/explain their mental-imagery based BCI performances ?

International audienceMental-Imagery based Brain-Computer Interfaces (MI-BCIs) make use of brain signals produced during mental imagery tasks to control a computerised system. The current unreliability of MI-BCIs could be due, at least in part, to the use of inappropriate user-training procedures. In order to improve these procedures , it is necessary first to understand the mechanisms underlying MI-BCI user-training, notably through the identification of the factors influencing it. Thus, this paper aims at creating a statistical model that could explain/predict the performances of MI-BCI users using their traits (e.g., personality). We used the data of 42 participants (i.e., 180 MI-BCI sessions in total) collected from three different studies that were based on the same MI-BCI paradigm. We used machine learning regressions with a leave-one-subject-out cross validation to build different models. Our first results showed that using the users' traits only may enable the prediction of performances within one multiple-session experiment, but might not be sufficient to reliably predict MI-BCI performances across experiments

Standardization of Protocol Design for User Training in EEG-based Brain-Computer Interface

International audienceBrain-computer interfaces (BCIs) are systems that enable a personto interact with a machine using only neural activity. Such interaction canbe non-intuitive for the user hence training methods are developed to increaseone’s understanding, confidence and motivation, which would in parallel increasesystem performance. To clearly address the current issues in the BCI usertraining protocol design, here it is divided intointroductoryperiod and BCIinteractionperiod. First, theintroductoryperiod (before BCI interaction) mustbe considered as equally important as the BCI interaction for user training. Tosupport this claim, a review of papers show that BCI performance can dependon the methodologies presented in such introductory period. To standardize itsdesign, the literature from human-computer interaction (HCI) is adjusted to theBCI context. Second, during the user-BCI interaction, the interface can takea 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 thatmotor neurons can be triggered from a simple observation of certain objects, anddepending on objects’ properties (size, location etc.) neural reactions can varygreatly. Surprisingly, the effects of perceptual affordance were not investigatedin the BCI context. Both inconsistent introductions to BCI as well as variableinterface designs make it difficult to reproduce experiments, predict their outcomesand compare results between them. To address these issues, a protocol designstandardization for user training is proposed