Vers des interfaces cérébrales adaptées aux utilisateurs : interaction robuste et apprentissage statistique basé sur la géométrie riemannienne. (Toward user-adapted brain computer interfaces : robust interaction and machine learning based on riemannian geometry)

Au cours des deux dernieres decennies, l'interet porte aux interfaces cerebrales ou Brain Computer Interfaces (BCI) s’est considerablement accru, avec un nombre croissant de laboratoires de recherche travaillant sur le sujet. Depuis le projet Brain Computer Interface, ou la BCI a ete presentee a des fins de readaptation et d'assistance, l'utilisation de la BCI a ete etendue a d'autres applications telles que le neurofeedback et l’industrie du jeux video. Ce progres a ete realise grâce a une meilleure comprehension de l'electroencephalographie (EEG), une amelioration des systemes d’enregistrement du EEG, et une augmentation de puissance de calcul.Malgre son potentiel, la technologie de la BCI n’est pas encore mature et ne peut etre utilise en dehors des laboratoires. Il y a un tas de defis qui doivent etre surmontes avant que les systemes BCI puissent etre utilises a leur plein potentiel. Ce travail porte sur des aspects importants de ces defis, a savoir la specificite des systemes BCI aux capacites physiques des utilisateurs, la robustesse de la representation et de l'apprentissage du EEG, ainsi que la suffisance des donnees d’entrainement. L'objectif est de fournir un systeme BCI qui peut s’adapter aux utilisateurs en fonction de leurs capacites physiques et des variabilites dans les signaux du cerveau enregistres.A ces fins, deux voies principales sont explorees : la premiere, qui peut etre consideree comme un ajustement de haut niveau, est un changement de paradigmes BCI. Elle porte sur la creation de nouveaux paradigmes qui peuvent augmenter les performances de la BCI, alleger l'inconfort de l'utilisation de ces systemes, et s’adapter aux besoins des utilisateurs. La deuxieme voie, consideree comme une solution de bas niveau, porte sur l’amelioration des techniques de traitement du signal et d’apprentissage statistique pour ameliorer la qualite du signal EEG, la reconnaissance des formes, ainsi que la tache de classification.D'une part, une nouvelle methodologie dans le contexte de la robotique d'assistance est definie : il s’agit d’une approche hybride ou une interface physique est complementee par une interface cerebrale pour une interaction homme-machine plus fluide. Ce systeme hybride utilise les capacites motrices residuelles des utilisateurs et offre la BCI comme un choix optionnel : l'utilisateur choisit quand utiliser la BCI et peut alterner entre les interfaces cerebrales et musculaire selon le besoin.D'autre part, pour l’amelioration des techniques de traitement du signal et d'apprentissage statistique, ce travail utilise un cadre Riemannien. Un frein majeur dans le domaine de la BCI est la faible resolution spatiale du EEG. Ce probleme est du a l'effet de conductance des os du crâne qui agissent comme un filtre passe-bas non lineaire, en melangeant les signaux de differentes sources du cerveau et reduisant ainsi le rapport signal-a-bruit. Par consequent, les methodes de filtrage spatial ont ete developpees ou adaptees. La plupart d'entre elles – a savoir la Common Spatial Pattern (CSP), la xDAWN et la Canonical Correlation Analysis (CCA) – sont basees sur des estimations de matrice de covariance. Les matrices de covariance sont essentielles dans la representation d’information contenue dans le signal EEG et constituent un element important dans leur classification. Dans la plupart des algorithmes d'apprentissage statistique existants, les matrices de covariance sont traitees comme des elements de l'espace euclidien. Cependant, etant symetrique et defini positive (SDP), les matrices de covariance sont situees dans un espace courbe qui est identifie comme une variete riemannienne. Utiliser les matrices de covariance comme caracteristique pour la classification des signaux EEG, et les manipuler avec les outils fournis par la geometrie de Riemann, fournit un cadre solide pour la representation et l'apprentissage du EEG.

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