Single-trial EEG classification using in-phase
average for brain-computer interface

doi:10.1007/s11460-008-0034-2

Front. Electr. Electron. Eng.

2008, Vol. 3

Issue (2) : 194-197 https://doi.org/10.1007/s11460-008-0034-2

Single-trial EEG classification using in-phase average for brain-computer interface

GUAN Jin‘an, CHEN Yaguang

School of Electronic Engineering, South-Central University for Nationalities;

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Abstract Communication signals should be estimated by a single trial in a brain-computer interface. Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electroencephalogram (EEG), this paper adopted the time-lock averaged signals from multi-channels as features. 200 trials of EEG recordings evoked by target or non-target stimuli were classified by the support vector machine (SVM). Results show that a classification accuracy of higher than 97% can be obtained by merely using the 250–550 ms time section of the averaged signals with channel Cz and Pz as features. It suggests that a possible approach to boost communication speed and simplify the designation of the brain-computer interface (BCI) system is worthy of an attempt in this way.

Issue Date: 05 June 2008

Cite this article:

an,GUAN Jin‘,CHEN Yaguang. Single-trial EEG classification using in-phase average for brain-computer interface[J]. Front. Electr. Electron. Eng., 2008, 3(2): 194-197.

URL:

https://academic.hep.com.cn/fee/EN/10.1007/s11460-008-0034-2
https://academic.hep.com.cn/fee/EN/Y2008/V3/I2/194

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