Abstract：A new P300 EEG recognition algorithm is proposed. Complex operation and miscellaneous data of multichannel and multi-featured are avoided. First, aiming at selecting optimum wavelet base, which is lack of theoretical basis in wavelet transform of P300, a method is proposed based on SNR and RMSE, and the noise of original signals coherent averaged is removed combining the result of SPWD time and frequency analysis, and observations are decomposed by the preferred JADE algorithm. Then, aiming at selecting automatically and avoiding excess decomposition after BSS of P300, combining G1, temporal and spatial analysis model is built, P300 component is optimum extracted automatically and mapped to electrodes. Finally, in order to improve BCI system application online, combining EA with SFFS, and the training model of 6-dimension feature vector is built so that classified and recognized by C-SVM. As is shown by the experimental results, compared with traditional data processing technique, the effect of P300 component extraction, accuracy and speed of system recognition are improved visibly.
胡春海,信思旭,刘斌,刘永红. 基于小波变换和盲源分离的P300识别算法研究[J]. 计量学报, 2017, 38(2): 242-246.
HU Chun-hai,XIN Si-xu,LIU Bin,LIU Yong-hong. Study on Recognition Algorithm of P300 Based on Wavelet Transform and Blind Source Separation. Acta Metrologica Sinica, 2017, 38(2): 242-246.
2017, Vol. 38 
