An EEG classification method based on entropy of dynamic functional connectivity

Abstract

The invention discloses an EEG classification method based on the entropy value of dynamic functional connection. First, the obtained original EEG signals are preprocessed, and then filtered; then the phase synchronization analysis method is used to calculate the EEG signals of each frequency band at each time. Point the phase relationship between each two channels to obtain the dynamic functional connection matrix; then calculate the time domain entropy of the phase relationship value between the two channels one by one to obtain the information entropy of each edge to measure the time of each edge of the EEG functional network The complexity of the domain; the dynamic functional connection entropy of each frequency band is used as the classification feature of the EEG functional network, and the adaptive improvement classifier is trained to obtain multiple adaptive improvement classifiers and the corresponding classification accuracy; The samples are combined for classification. The problem of low classification accuracy in the existing EEG signal classification methods is solved.

Description

technical field [0001] The invention belongs to the technical field of EEG signals, in particular to an EEG classification method based on the entropy value of dynamic functional connections. Background technique [0002] As a combination of electroencephalogram (EEG) technology and complex network theory, EEG signal data classification methods have become one of the hotspots in the field of brain science. However, due to the limitation of the principle of the traditional EEG signal data classification method, the classification accuracy is low, which seriously affects its application value. [0003] Traditional EEG signal data classification methods mainly include: time-domain-based analysis methods, frequency-domain-based analysis methods, and time-frequency analysis methods. Classification features are obtained after spectrum analysis. These two methods have strict requirements for EEG signal preprocessing, and EEG is a non-stationary signal. Using these two methods will...

AI Technical Summary

Problems solved by technology

[0005] The object of the present invention is to provide a kind of EEG classification method based on the entropy value of dynamic functional connection, which solves the problem of low classification accuracy in the existing EEG signal classification method

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