Abstract
N400 is a kind of event-related potential (ERP), which is related to language processing of brain and can be used for the evaluation of clinical psychological diseases. There still remain some problems in the accurate N400 waveform extraction from fewer-trial EEG data under the low signal-to-noise ratio (SNR)level. In this study, a supervised signal-to-noise ratio maximizer (SSM)method to obtain N400 waveform from multi-channel EEG data is proposed. The SSM algorithm designs a spatial filter for low-rank ERP component and extracts the N400 by 40-trial EEG datasets of each subject. The algorithm has more excellent performance in estimating the accurate N400 waveform from simulation data and real EEG data, compared to SIM and the regularized SOBI algorithms. The results show that the proposed method can effectively achieve the N400 extraction from fewer-trial EEG data.
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