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

Frontiers of Electrical and Electronic Engineering in China 2008年第2期3卷 194-197页

作者： Jin’an GUAN Yaguang CHEN School of Electronic Engineering South-Central University for NationalitiesWuhan 430074China

Communication signals should be estimated by a single trial in a brain-computer *** the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electro-encephalogram(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 *** 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.

关键词： in-phase average visual evoked potentials brain-computer interfaces single-trial estimation

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N2 Components as Features for Brain Computer Interface

N2 Components as Features for Brain Computer Interface

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作者： Jinan Guan, Yaguang Chen, Jiarui Lin, YunYuan, Ming Huang Institute of Biomedical Information and Control, School of Life Science, Huazhong University of Science and Technology, Wuhan, 430074 China School of Electronic Engineering, South-Central University for Nationalities, Wuhan, 430074, China

A mental speller using brain computer interface (BCI) may allow a user to communicate by gazing a virtual keyboard on the screen to select a desired character to compose a word, and thus sentences. Difference from other paradigms, a so called Imitating-Natural-Reading (INR) modality was exploited to construct a novel mental speller-INR SPELLER. In order to boost the bit rate, a 300ms window was used to estimate the accurate time of target stimuli onset from EEG signals. To meet this task, N2 components of visual evoked potentials (VEP) were investigated. Experimental results indicated that the object-specified component can be estimated in single trial at an accuracy of 90.5% with Support Vector Machine (SVM) classifier.

关键词： Brain computer interface (BCI) N2 single-trial estimation visual evoked potential (VEP).

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