Linear filtering; Specific periodic components; Generalized Rayleigh Quotient; Source separation; Steady-states; EEG
Abstract :
[en] Extracting relevant information from noisy multidimensional signals has tremendous impacts in numerous applications, ranging from audio separation to electrophysiological recording analysis. Linear filters are often considered to reconstruct and interpret the latent sources generating the data. Known properties of the sources can be used to guide their separation. In neuroscience, the cortical processes underlying perception in different modalities (visual, auditory, ...) is often studied using electroencephalography (EEG) during periodic stimulation, eliciting periodic activity in neural sources, some of which being specific to the considered modality. Whereas current approaches extract sources either periodic or discriminative, none of them accounts for both aspects at once. This paper proposes several methods extracting periodic sources specific between two classes, hence termed as Linear Periodic Discriminant Analysis methods. They are validated on synthetic data and EEG recordings of subjects to whom periodic stimulation from two modalities is applied. The methods highlight modality-specific periodic responses.
Disciplines :
Neurology
Author, co-author :
Mulders, Dounia; Université Catholique de Louvain - UCL
de Bodt, Cyril; Université Catholique de Louvain - UCL
Lejeune, Nicolas ; Université de Liège - ULiège > Form. doct. sc. méd. (paysage)
Mouraux, André; Université Catholique de Louvain - UCL > Institute of NeuroScience
Verleysen, Michel; Université Catholique de Louvain - UCL
Language :
English
Title :
Linear Periodic Discriminant Analysis of Multidimensional Signals
Publication date :
2018
Event name :
International Conference on Neural Information Processing
Event place :
Siem Reap, Cambodia
Event date :
du 13/12/2018 au 16/12/2018
Audience :
International
Main work title :
Neural Information Processing. ICONIP 2018.
Author, co-author :
Cheng, L.
Leung, A.
Ozawa, S.
Collection name :
Lecture Notes in Computer Science, vol 11306. Springer, Cham
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