Multiway canonical correlation analysis for frequency components recognition in SSVEP-based BCIs

Yu Zhang, Guoxu Zhou, Qibin Zhao, Akinari Onishi, Jing Jin, Xingyu Wang, Andrzej Cichocki

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

116 Citations (Scopus)

Abstract

Steady-state visual evoked potential (SSVEP)-based brain computer-interface (BCI) is one of the most popular BCI systems. An efficient SSVEP-based BCI system in shorter time with higher accuracy in recognizing SSVEP has been pursued by many studies. This paper introduces a novel multiway canonical correlation analysis (Multiway CCA) approach to recognize SSVEP. This approach is based on tensor CCA and focuses on multiway data arrays. Multiple CCAs are used to find appropriate reference signals for SSVEP recognition from different data arrays. SSVEP is then recognized by implementing multiple linear regression (MLR) between EEG and optimized reference signals. The proposed Multiway CCA is verified by comparing to the standard CCA and power spectral density analysis (PSDA). Results showed that the Multiway CCA achieved higher recognition accuracy within shorter time than that of the CCA and PSDA.

Original languageEnglish
Title of host publicationNeural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Pages287-295
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China
Duration: 13 Nov 201117 Nov 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7062 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Neural Information Processing, ICONIP 2011
Country/TerritoryChina
CityShanghai
Period13/11/1117/11/11

Keywords

  • Brain-computer interface (BCI)
  • Canonical Correlation Analysis (CCA)
  • Electroencephalogram (EEG)
  • Steady-State Visual Evoked Potential (SSVEP)
  • Tensor Decomposition

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