A Feature Selection Method of EEG Signal Based on Decision Tree

Abstract

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.

Description

technical field [0001] The invention relates to a method for analyzing electroencephalogram signals in electroencephalogram research, in particular to a method for selecting features of electroencephalogram signals. Background technique [0002] The human brain is a very complex system. Electroencephalograph (EEG) is the discharge activity of brain neuron cells collected through scalp-covered electrodes and conductive media, and contains a large amount of information that characterizes the physiological and psychological state of the human body. The study of EEG signals is one of the most cutting-edge fields in today's scientific research, involving the acquisition, preprocessing, processing and application of EEG signals, etc. Research in many fields such as pathophysiology, information and signal processing, computer science, biotechnology, biomedical engineering and even applied mathematics plays an extremely important role. With the increasing popularity of brain scienc...

AI Technical Summary

Problems solved by technology

This method has two disadvantages: (1) relying on subjective experience to select electrodes may easily cause information deviation; (2) manual operation is complicated, time-consuming and labor-intensive

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