Graph Regularized Tensor Factorization for Single-Trial EEG Analysis

This study proposes a tensor factorization algorithm for electroencephalographies (EEGs) that incorporates the geometric structure of the electrode location. The purpose is removing noise caused by EEG activities which are irrelevant to stimuli presented to a subject from single-trial event-related...

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Bibliographic Details
Published in2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 846 - 850
Main Authors Maki, Hayato, Tanaka, Hiroki, Sakti, Sakriani, Nakamura, Satoshi
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.04.2018
Subjects
Electrodes
Electroencephalography
Laplace equations
Matrix decomposition
Scalp
Tensile stress
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ISSN2379-190X
DOI10.1109/ICASSP.2018.8461897

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Summary:This study proposes a tensor factorization algorithm for electroencephalographies (EEGs) that incorporates the geometric structure of the electrode location. The purpose is removing noise caused by EEG activities which are irrelevant to stimuli presented to a subject from single-trial event-related potential (ERP) data. Canonical polyadic decomposition (CPD) is extended by adding a regularization term that controls the spatial smoothness of the decomposed components on a scalp. An initialization method using geometrical information is also proposed. The geometric structure of an EEG signal is expressed as an undirected graph where the similarities between electrodes are defined by their relative distances on a scalp. The effectiveness is demonstrated in a noise-removing experiment using pseudo-ERP, where the proposed method achieved better performance than the conventional CPD.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8461897