Thèse, AO, AO, Engineering Sciences, Signal and Image Processing, Traitement du signal et de l'image

Addressing the Large Variability of EEG Data with Riemannian Geometry : Toward Designing Reliable Brain-Computer Interfaces

Maria Sayu Yamamoto. Addressing the Large Variability of EEG Data with Riemannian Geometry : Toward Designing Reliable Brain-Computer Interfaces. Machine Learning [cs.LG]. Université Paris-Saclay, 2024. English. ⟨NNT : 2024UPASG098⟩. ⟨tel-04967163⟩

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