Tensor based simultaneous feature extraction and sample weighting for EEG classification

Authors:
Yoshikazu Washizawa;Hiroshi Higashi;Tomasz Rutkowski;Toshihisa Tanaka;Andrzej Cichocki
Affiliations:
RIKEN Brain Science Institute, Japan;Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Japan and RIKEN Brain Science Institute, Japan;RIKEN Brain Science Institute, Japan;Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Japan and RIKEN Brain Science Institute, Japan;RIKEN Brain Science Institute, Japan
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

Citing 2
Cited 0

A Multilinear Singular Value Decomposition

SIAM Journal on Matrix Analysis and Applications
MPCA: Multilinear Principal Component Analysis of Tensor Objects

IEEE Transactions on Neural Networks

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Abstract

In this paper we propose a Multi-linear Principal Component Analysis (MPCA) which is a new feature extraction and sample weighting method for classification of EEG signals using tensor decomposition. The method has been successfully applied to Motor-Imagery Brain Computer Interface (MI-BCI) paradigm. The performance of the proposed approach has been compared with standard Common Spatial Pattern (CSP) as well with a combination of PCA and CSP methods. We have achieved an average accuracy improvement of two classes classification in a range from 4 to 14 percents.