Research on Multi-domain Features Selection Algorithm of EEG Signals in Color Sensing

As the research on the identification of electroencephalograph (EEG) signals in color sensing is still rare, In this paper, the method of using the random forest algorithm to optimally combine the time domain features and frequency domain features of signals is proposed to identify the EEG signals in different color perceptions.. Firstly, the wavelet transform was used to decompose EEG signals by 7 layers, then extracted the average energy of EEG signals in delta, thelta, alpha and beta bands based on wavelet coefficients, and combined the statistic skewness and kurtosis to form feature vectors. In order to reduce computation load and delete redundant features, this paper proposed an feature selection algorithm based on random forest. And the adaptive boosting (AdaBoost) algorithm was adopted to classify the EEG signals. Results show that the average accuracy of the proposed algorithm can achieve 85.07%. And it proves that this algorithm proposed can classify the EEG signals based on different colors stimuli well, and get a better recognition rate.