Discrimination between idle and work states in BCI based on SSVEP

Author

Wang, Niya ; Qian, Tianyi ; Zhuo, Qing ; Gao, Xiaorong

Author_Institution

Tsinghua Univ., Beijing, China

Volume

4

fYear

2010

fDate

27-29 March 2010

Firstpage

355

Lastpage

358

Abstract

We present a novel method for idle and work states classification in brain computer interface (BCI) based on steady-state visual evoked potentials (SSVEP). Canonical correlation analysis (CCA) and maximum contrast combination (MCC) are used to extract features of electroencephalogram (EEG) signals. The correlation coefficients from CCA and SNR from MCC were classified by a linear classifier. Then an extra strategy of excluding alpha wave interference helped improve the classification accuracy. This method had a good performance in real EEG signals.

Keywords

brain-computer interfaces; correlation methods; electroencephalography; feature extraction; interference (signal); medical signal processing; signal classification; visual evoked potentials; EEG; alpha wave interference; brain computer interface; canonical correlation analysis; electroencephalogram; feature extraction; linear classifier; maximum contrast combination; steady state visual evoked potentials; work states classification; Brain computer interfaces; Classification algorithms; Electroencephalography; Feature extraction; Frequency; Interference; Power harmonic filters; Signal analysis; Signal to noise ratio; Silicon compounds; Alpha Wave Detection; Brain Computer Interface; Canonical Correlatoin Analysis; Maximum Contrast Combination;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computer Control (ICACC), 2010 2nd International Conference on

Conference_Location

Shenyang

Print_ISBN

978-1-4244-5845-5

Type

conf

DOI

10.1109/ICACC.2010.5486907

Filename

5486907