Detection of steady-state visual-evoked potential using differential canonical correlation analysis

Chun-Shu Wei, Yuan Pin Lin, Yijun Wang, Yu Te Wang, Tzyy Ping Jung

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

22 Scopus citations

Abstract

Steady-state visual evoked potential (SSVEP) is an electroencephalogram (EEG) activity elicited by periodic visual flickers. Frequency-coded SSVEP has been commonly adopted for functioning brain-computer interfaces (BCIs). Up to date, canonical correlation analysis (CCA), a multivariate statistical method, is considered to be state-of-the-art to robustly detect SSVEPs. However, the spectra of EEG signals often have a 1/f power-law distribution across frequencies, which inherently confines the CCA efficiency in discriminating between high-frequency SSVEPs and low-frequency background EEG activities. This study proposes a new SSVEP detection method, differential canonical correlation analysis (dCCA), by incorporating CCA with a notch-filtering procedure, to alleviate the frequency-dependent bias. The proposed dCCA approach significantly outperformed the standard CCA approach by around 6% in classifying SSVEPs at five frequencies (9-13Hz). This study could promote the development of high-performance SSVEP-based BCI systems.

Original languageEnglish
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages57-60
Number of pages4
DOIs
StatePublished - 1 Dec 2013
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: 6 Nov 20138 Nov 2013

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Country/TerritoryUnited States
CitySan Diego, CA
Period6/11/138/11/13

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