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Abstract

The present invention relates to a method for selecting features of EEG signals based on a decision tree: firstly, acquired multi-channel EEG signals are pre-processed, and then the pre-processed EEG signals are performed with feature extraction by utilizing principal component analysis, to obtain a analysis data set matrix with decreased dimensions; superior column vectors are obtained through analyzing from the analysis data set matrix with decreased dimensions by utilizing a decision tree algorithm, and all the superior column vectors are jointed with the number of the columns increased and the number of the rows unchanged, to be reorganized into a final superior feature data matrix; finally, the reorganized superior feature data matrix is input to a support vector machine (SVM) classifier, to perform a classification on the EEG signals, to obtain a classification accuracy. In the present invention, superior features are selected by utilizing a decision tree, to avoid influence of subjective factors during the selection, so that the selection is more objective and with a higher classification accuracy. The average classification accuracy through the present invention may reach 89.1%, increased by 0.9% compared to the conventional superior electrode reorganization.

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First Claim