Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs

被引：350

作者：

Lin, Zhonglin ^{[1
]}

Zhang, Changshui

Wu, Wei

Gao, Xiaorong

机构：

[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

[2] Tsinghua Univ, Sch Med, Dept Biomed Engn, Beijing 100084, Peoples R China

来源：

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2007年 / 54卷 / 06期

关键词：

brain computer interface; canonical correlation analysis; electroencephalogram; steady-state visual evoked potentials;

D O I：

10.1109/TBME.2006.889197

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those. using a widely used FFT (fast Fourier transform)-based spectrum estimation method.

引用

页码：1172 / 1176

页数：5

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