Single-trial EEG classification using in-phase average for brain-computer interface |
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Authors: | Jin an Guan Yaguang Chen |
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Affiliation: | (1) School of Electronic Engineering, South-Central University for Nationalities, Wuhan, 430074, China |
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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. __________ Translated from Journal of Huazhong University of Science and Technology (Nature Science Edition), 2007, 35(1): 11–13 [译自: 华中科技大学学报(自然科学版)] |
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Keywords: | in-phase average visual evoked potentials brain-computer interfaces single-trial estimation |
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