Electroencephalogram feature selection approach based on decision-making tree

The invention relates to an electroencephalogram feature selection approach based on a decision-making tree. Firstly, collected multi-channel electroencephalograms are preprocessed; secondly, feature extraction is conducted on the preprocessed electroencephalograms by means of the principal component analysis method to obtain feature vectors; thirdly, the feature vectors obtained after the step of feature extraction is conducted are input into the decision-making tree, and superior feature selection is conducted; fourthly, superior features selected by the decision-making tree are reassembled; finally, the reassembled superior feature vectors are input into a support vector machine, and electroencephalogram classification is conducted to obtain the classification correct rate. According to the electroencephalogram feature selection approach, the decision-making tree is used for superior feature selection, operation is simple, manual participation is not needed, and time and labor are saved. The decision-making tree is used for superior feature selection, so that influence of subjective factors of people is avoided in a selection process, selection is more objective, and the classification correct rate is higher. Experiments show that the average accuracy is 89.1% by using the approach to conduct electroencephalogram classification and is increased by 0.9% compared with a traditional superior electrode reassembling method.