A Feature Extraction Method Based on Multi-rhythmic and Co-space Modes for P300 Potential

Abstract

In order to extract the effective features of EEG data based on P300 potential and improve the classification accuracy of brain-computer interface (BCI) system, a novel method from spatial and frequency domain is proposed in this paper. That is, the multi-rhythm signal and Common Spatial Pattern (CSP) are combined to extract the feature of P300 potential. Meanwhile, this paper uses the elastic networks as classifier which combines with $l_{1}$ norm and $l_{2}$ norm that not only makes the coefficients sparse, but also avoids losing the inhere t structure among samples from the same class. In this paper, the public EEG data set is used to verify the proposed method and by comparing the traditional CSP method. The experimental results show that the new method provides more frequency-space-related feature information increased by 4.8% compared with the traditional CSP method. It indicates that the proposed method achieves the good performance for the P300 EEG analysis.