Tensor based simultaneous feature extraction and sample weighting for EEG classification

Yoshikazu Washizawa, Hiroshi Higashi, Tomasz Rutkowski, Toshihisa Tanaka, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publicationModels and Applications - 17th International Conference, ICONIP 2010, Proceedings
Pages26-33
Number of pages8
EditionPART 2
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia
Duration: 22 Nov 201025 Nov 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6444 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Neural Information Processing, ICONIP 2010
Country/TerritoryAustralia
CitySydney, NSW
Period22/11/1025/11/10

Keywords

  • classification
  • Feature extraction
  • multi-linear PCA
  • tensor decomposition

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