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.
GUAN Jin‘an, CHEN Yaguang
. Single-trial EEG classification using in-phase
average for brain-computer interface[J]. Frontiers of Electrical and Electronic Engineering, 2008
, 3(2)
: 194
-197
.
DOI: 10.1007/s11460-008-0034-2
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