Slice oriented tensor decomposition of EEG data for feature extraction in space, frequency and time domains

Qibin Zhao, Cesar F. Caiafa, Andrzej Cichocki, Liqing Zhang, Anh Huy Phan

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

10 Citations (Scopus)

Abstract

In this paper we apply a novel tensor decomposition model of SOD (slice oriented decomposition) to extract slice features from the multichannel time-frequency representation of EEG signals measured for MI (motor imagery) tasks in application to BCI (brain computer interface). The advantages of the SOD based feature extraction approach lie in its capability to obtain slice matrix components across the space, time and frequency domains and the discriminative features across different classes without any prior knowledge of the discriminative frequency bands. Furthermore, the combination of horizontal, lateral and frontal slice features makes our method more robust for the outlier problem. The experiment results demonstrate the effectiveness of our method.

Original languageEnglish
Title of host publicationNeural Information Processing - 16th International Conference, ICONIP 2009, Proceedings
Pages221-228
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Thailand
Duration: 1 Dec 20095 Dec 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5863 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Neural Information Processing, ICONIP 2009
Country/TerritoryThailand
CityBangkok
Period1/12/095/12/09

Keywords

  • BCI
  • EEG
  • Tensor decomposition

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