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

研究成果: Conference contribution同行評審

22 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
頁面57-60
頁數4
DOIs
出版狀態Published - 1 12月 2013
事件2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
持續時間: 6 11月 20138 11月 2013

出版系列

名字International IEEE/EMBS Conference on Neural Engineering, NER
ISSN(列印)1948-3546
ISSN(電子)1948-3554

Conference

Conference2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
國家/地區United States
城市San Diego, CA
期間6/11/138/11/13

指紋

深入研究「Detection of steady-state visual-evoked potential using differential canonical correlation analysis」主題。共同形成了獨特的指紋。

引用此