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            <title xml:lang="en">Methods for multi-subject electroencephalography and application to brain-computer interfaces</title>
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            <idno type="halRefHtml">Neurosciences. Université Grenoble Alpes, 2018. Français. &lt;a target="_blank" href="https://www.theses.fr/"&gt;&amp;#x27E8;NNT : &amp;#x27E9;&lt;/a&gt;</idno>
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                <title xml:lang="en">Methods for multi-subject electroencephalography and application to brain-computer interfaces</title>
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                <term xml:lang="en">Electroencephalography (EEG)</term>
                <term xml:lang="en">Blind Source Separation (BSS)</term>
                <term xml:lang="en">Hyperscanning</term>
                <term xml:lang="en">Brain- Computer Interface (BCI)</term>
                <term xml:lang="en">Classification</term>
                <term xml:lang="en">Event-Related Potential (ERP)</term>
                <term xml:lang="fr">Potentiels Évoqués</term>
                <term xml:lang="fr">Interface cerveau-ordinateur</term>
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                <term xml:lang="fr">Électroencéphalographie</term>
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              <p>The study of several brains interacting (hyperscanning) with neuroimagery allows to extend our understanding of social neurosciences. This work is organized across three interdependent contributions for the field. We propose a framework for hyperscanning using multi-user Brain-Computer Interfaces (BCI) that includes several social paradigms such as cooperation or competition. To do so, we developed for our first contribution an experimental platform Brain Invaders 2, it consists of a multi-player video game controlled by calibration-free classification of visual event related potentials (ERP) recorded by electroencephalography (EEG). The plateform is validated through two experimental protocols including nineteen and twenty two pairs of subjects while using different adaptive classification approaches using Riemannian geometry.Those approaches are theoretically and experimentally compared during the second contribution; we demonstrates the superiority in term of accuracy of merging independent classifications over the classification of the hyperbrain.Analysis of inter-brain synchronizations is a common approach for hyperscanning, however it is challenging for transient EEG waves with low signal-to-noise ratio such as ERP. Therefore, we propose a new blind source separation model during the third contribution, namely composite model, to extract simultaneously evoked EEG sources and ongoing EEG sources. A solution using approximate joint diagonalization is given and implemented with a fast Jacobi-like algorithm. We demonstrates on Brain Invaders 2 data that our model performs better in term of accuracy and robustness compared to the existing models.</p>
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              <p>L'étude par neuro-imagerie de l'activité de plusieurs cerveaux en interactions (hypercanning) permet d'étendre notre compréhension des neurosciences sociales. Ces travaux sont organisées en trois contributions interdépendantes.Nous proposons un cadre pour l'hypercanning utilisant les interfaces cerveau-ordinateur multi-utilisateur qui inclut différents paradigmes sociaux tels que la coopération ou la compétition. Pour notre première contribution, nous avons développé une plateforme expérimentalesous la forme d'un jeu vidéo multijoueur contrôlé par la classification sans calibration de potentiels évoqués visuels enregistrés par électroencéphalographie (EEG), nommée Brain Invaders 2. Cette plateforme est validée par deux protocoles expérimentaux comprenant dix-neuf et vingt-deux paires de sujets et utilise différentes approches de classification adaptative par géométrie riemannienne. Ces approches sont théoriquement et expérimentalement comparées et nous montrons la supériorité de la fusion des classifieurs indépendants sur la classification d'un hypercerveau durant la seconde contribution. L'analyse de coïncidence des signaux entre les individus est une approche classique pour l'hyperscanning, elle estpourtant difficile quand les signaux EEG concernés sont transitoires avec un faible rapport signal-à-bruit. En troisième contribution, nous proposons un nouveau modèle composite de séparation aveugle de sources physiologiquement plausibles permettant de compenser cette variabilité. Une solution par diagonalisation conjointe approchée est proposée avec une implémentation d'un algorithme de type Jacobi. A partir des données de Brain Invaders 2, nous montrons que cette solution permet d'extraire simultanément des sources d'artéfacts, des sources d'EEG évoquées et des sources d'EEG continues avec plus de robustesse et de précision que les modèles existants.</p>
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