Method for classifying electroencephalogram (EEG) signals based on multi-scale brain function network

Abstract

The invention relates to a method for classifying electroencephalogram (EEG) signals based on a multi-scale brain function network. The method comprises the steps that data are acquired, specifically,the EEG signals in a resting state are collected and preprocessed; multi-scale time series are calculated, specifically, multi-scale processing is conducted on preprocessed time series of all leads,and thus the generalized multi-scale coarse-grained time series are obtained; the multi-scale brain function network is built, specifically, the amplitude correlation degree and the phase correlationdegree of all the leads of the EEG signals are taken as the quantitative standards to calculate multi-scale weighted brain function networks; a multi-scale convolutional neural network capable of learning the multi-scale brain function network is built; and neural network training is conducted, specifically, the cross-correlation degree of decision errors generated by all convolutional neural network paths is taken as a penalty term and a penalty loss function to accelerate decision-making of the neural network and reduce the generalization error.

Description

technical field [0001] The invention relates to the construction of a multi-scale brain function network based on electroencephalogram (EEG) signals, and the neural network design of multi-scale decision-making with multi-path convolutional neural network and decision-making fusion with fuzzy neural network. It is a multi-scale information-based Fusion realizes the method of EEG signal classification. Background technique [0002] Electroencephalographic (EEG) tracing is an electrophysiological monitoring technique that records electrical activity in the brain, which reflects voltage fluctuations generated by ionic currents within neurons in the brain. Neurons are constantly exchanging ions with their extracellular environment. Similar ions repel each other. When many ions are pushed out from many neurons simultaneously, they push neighboring ions, which in turn push their neighboring ions, forming "waves," When the ion wave reaches the scalp, it pushes and pulls the electr...

AI Technical Summary

Problems solved by technology

The essence of establishing a functional brain network is to find a way to describe the degree of correlation or synchronization between channels or brain intervals. The review article A critical review:coupling and synchronization analysis methods of EEG signal with mild cognitive impairment published by Wen's team in 2015 summarizes the methods used for There are 5 coupling methods and 6 synchronization algorithms for assessing brain degeneration. The existing methods can be attributed to the phase correlation-based functional connectivity construction method or the amplitude correlation-based functional connectivity construction method. The sensitivity of the EEG signal in the degenerated state shows an average value of about 70% to 80%, and there is still a lot of room for improvement

Images