Des Signaux Cérébraux Aux Activités Cognitives, Approches Géométriques Et Apprentissage Statistique Sylvain Chevallier

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Des Signaux Cérébraux Aux Activités Cognitives, Approches Géométriques Et Apprentissage Statistique Sylvain Chevallier Des signaux cérébraux aux activités cognitives, approches géométriques et apprentissage statistique Sylvain Chevallier To cite this version: Sylvain Chevallier. Des signaux cérébraux aux activités cognitives, approches géométriques et appren- tissage statistique. Traitement du signal et de l’image [eess.SP]. Université de Versailles Saint Quentin en Yvelines; Université Paris-Saclay, 2019. tel-02542067 HAL Id: tel-02542067 https://tel.archives-ouvertes.fr/tel-02542067 Submitted on 15 Apr 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Universite´ de Versailles Saint-Quentin Laboratoire d'Ing´enieriedes Syst`emesde Versailles (LISV EA 4048) Habilitation `adiriger des recherches Discipline : G´enieinformatique, automatique et traitement du signal Pr´esent´eepar : Sylvain Chevallier Des signaux c´er´ebraux aux activit´escognitives, approches g´eom´etriques et apprentissage statistique Soutenue le 4 d´ecembre 2019 `aV´elizydevant le jury : M. Fran¸coisCabestaing, Professeur, Universit´ede Lille (rapporteur) Mme Anne Gu´erin-Dugu´e,Professeur, Universit´ede Grenoble (rapporteure) M. Laurent Perrinet, CR HDR, Institut de Neurosciences de la Timone (rapporteur) Mme Samia Bouchafa-Bruneau, Professeure, Universit´ed'Evry Val d'Essonne (pr´esidente) M. Fran¸coisRouthier, Professeur, Universit´eLaval, Qu´ebec (examinateur) M. Eric Monacelli, Professeur, Universit´ede Versailles St Quentin (tuteur) ii R´esum´e Les progres` en analyse des signaux cer´ ebraux´ pour un decodage´ en temps reel´ ont permis des avancees´ importantes pour les interfaces cer´ ebrales.´ Ces interfaces cer´ ebrales´ ou brain-computer interfaces (BCI) permettent de controlerˆ ou d’echanger´ des commandes avec un systeme` en necessitant´ pas ou peu de capacites´ physiques. Elles offrent une solution adaptee´ pour les personnes en situation de handicap. Dans leur forme actuelle, elles requierent` toutefois une bonne capacite´ de concentration pour fonctionner correctement. Ces BCI ont connu un rapide essor graceˆ aux methodes´ d’apprentissage statistique, mais elles font face a` deux verrous scientifiques. Le premier concerne la puissance du signal, qui est tres` faible, et les bruits environnants qui contaminent les enregistrements de signaux cer´ ebraux,´ avec des niveaux bien superieurs´ a` celui du signal d’inter´ et.ˆ Le second point concerne les variabilites´ individuelles qui peuvent reduire´ les algorithmes a` des predictions´ au niveau de la chance pour 10 a` 20% des sujets. Les travaux present´ es´ dans ce manuscrit abordent les aspects tant experi-´ mentaux que theoriques´ pour aborder ces deux problemes.` Les approches en traitement du signal utilisees´ pour travailler sur les signaux cer´ ebraux´ peuvent dans certains cas s’appliquer sur d’autres types series´ temporelles, par exemple pour des applications industrielles sur la detection´ d’ano- malie. Toutes ces approches sont developp´ ees´ avec des outils libres pour une science ouverte et diffusable, en apportant quand c’est possible des contributions aux logiciels libres. iii iv Abstract Real-time decoding of brain signals has opened new advances for brain interfaces thanks to dedicated signal analysis tools. Those brain-computer interfaces or BCI allow to control or to send commands to numerical sys- tems without relying on physical capability. These interfaces are thus well suited for people with disabilities, but they still require good concentration capabilities. The BCI have attracted a growing interest with the development of dedicated machine learning algorithms. However, two major scientific problems have been identified. First, the signal is very weak while there are multiple noise sources of higher energy. Second, the individual variations could lead the decoding algorithm to approach the chance level for 10 to 20 % of the subjects. The works presented in this manuscript describe the experimental and theoretical approach proposed to tackle those problems. The signal proces- sing approaches developed for brain signals analysis could be applied to wider class of time series, with possible industrial applications for anomaly detection. All these works are conducted by following the open science principles, relying on and extending open source tools each time it was possible. v vi Table des mati`eres R´esum´e iii Abstract iv 1 Curriculum Vitæ1 1.1 Encadrement doctoral....................... 3 1.2 Participation a` la vie scientifique................. 4 1.3 Publications............................. 13 1.4 Enseignements............................ 18 1.5 Conception d’enseignements et pratiques pedagogiques´ ... 18 2 Synth`esedes travaux 22 2.1 Les interfaces cer´ ebrales´ ...................... 25 2.2 Apprentissage de dictionnaires parcimonieux......... 27 2.3 Travaux experimentaux´ sur la SSVEP.............. 33 2.4 Approches Riemanniennes pour la BCI............. 38 2.5 Contributions applicatives et industrielles............ 43 3 Projets de recherche 46 3.1 Des interfaces au service de l’utilisateur............. 47 3.2 Une recherche inscrite dans l’Open Science ............ 49 3.3 Construction d’espaces de representation´ adaptes´ ....... 51 3.4 Architecture neuronale et apprentissage profond....... 54 3.5 Detection´ d’anomalies....................... 57 Bibliographie 59 vii Table des mati`eres viii Table des figures 2.1 Modele` de neurone pour la communication ephaptique´ ... 24 2.2 Principe d’une interface cer´ ebrale´ ................ 26 2.3 Dictionnaire univarie´ et multivarie´................ 29 2.4 Dictionnaire multivarie´ appris sur de l’EEG........... 33 2.5 Dispositif experimental´ pour la SSVEP............. 35 2.6 Geodesique´ et distance euclidienne pour les matrices de co- variance............................... 41 2.7 Classification dans l’espace des matrices SPD.......... 42 2.8 Trajectoire d’un sujet dans l’espace des matrices de covariance 43 2.9 Processus de Building Information Modeling.......... 44 3.1 Vision de l’open science par l’ERC................ 50 3.2 Echo state networks......................... 55 3.3 Illustration du transport optimal applique´ a` une distribution unimodale.............................. 58 ix 1 Curriculum Vitæ Maˆıtre de conferences,´ 39 ans, franc¸ais Coordonn´ees Administratives adresse : 10-12 avenue de l’Europe, 78140 Velizy´ tel.´ : 01 39 25 49 77 / 06 32 14 09 58 etablissement´ : LISV, Universite´ de Versailles Saint-Quentin courriel : [email protected] www : http://sites.google.com/site/sylvchev Personnelles adresse : 40 rue de Fontenay, 92330 Sceaux tel.´ : 09 80 95 27 69 Situation professionnelle Maˆıtre de conferences´ a` l’IUT de Velizy,´ Universite´ de Versailles Saint- • Quentin-en-Yvelines, depuis septembre 2011 (8 ans) Chercheur au Laboratoire d’Ingenierie´ des Systemes` de Versailles • (LISV) EA-4048 de Velizy,´ dans l’equipe´ Robotique Interactive CRCT accorde´ par le CNU de 2 semestres en 2018-2019 • 1 1 Curriculum Vitæ Formation 2009 Doctorat en informatique, Universite´ Paris-Sud Mention tres` honorable 2005 Master 2 recherche en informatique, Universite´ Paris-Sud Mention tres` bien 2004 Maˆıtrise de sciences cognitives, Universite´ Lyon 2 2003 Licence de sciences cognitives, Universite´ Lyon 2 2002 DEUG de sciences de la vie, Universite´ Paris-Sud, Orsay Exp´erienceprofessionnelle en enseignement et recherche 2011-auj. Maˆıtre de conferences´ IUT de Velizy´ Universite´ de Versailles Saint-Quentin-en-Yvelines 2011 Post-doctorat, LTCI, (6 mois) Tel´ ecom´ ParisTech Apprentissage automatique pour les interfaces cer´ ebrales´ 2010 Post-doctorat, LRI - equipe´ TAO INRIA-Saclay (6 mois) Reseaux´ de neurones profonds et interfaces cer´ ebrales´ 2008-2010 ATER a` mi-temps, laboratoire ETIS Universite´ de Cergy Emergence´ d’un biais attentionnel visuel dans l’interaction homme-robot 2005-2008 Bourse MENRT & monitorat, LIMSI Universite´ Paris-Sud Implementation´ d’un systeme` preattentionnel´ avec des neurones impulsionnels sous la direction de Ph. Tarroux et H. Paugam-Moisy 2 1 Curriculum Vitæ 1.1 Encadrement doctoral Hugo Martin, doctorant CIFRE UVSQ et Bouygues Construction — Encadrement a` 50%, finance´ par Bouygues, co-encadrement avec Eric Monacelli — Intitule´ de la these` : “Maquette numerique´ pour la construction : structurer et visualiser les connaissances metier”´ — Publications : [15], [17] — Soutenue le 7 decembre´ 2016 (duree´ : 3 ans et 7 mois) Emmanuel Kalunga, doctorant en cotutelle UVSQ et Tshwane University of Technology (Afrique du Sud) — Encadrement a` 40%, avec Eric Monacelli, financee´ sur projets — Intitule´ de la these` : “Toward User-adapted Brain-Computer Interfaces : Robust Interactions and Machine Learning based on Riemannian Geo- metry” — Publications : [1], [4], [12], [16], [18], [22], [23] — Soutenue le 30 aoutˆ 2017 (3 ans et 5 mois) Jinan Charafeddine, doctorante en cotutelle UVSQ et Universite´ Liba- naise (Liban) — Encadrement a` 30%, avec Didier Pradon et Samer Alfayad, financement libanais — Intitule´ de la these` : “Caracterisation´ et Integration´ des signaux mus- culaires pour le pilotage d’un exosquelette
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