Electroencephalogram signal classification method of artificial bee colony optimized BP neural network

Abstract

The invention discloses an electroencephalogram signal classification method of an artificial bee colony optimized BP neural network. The method is specifically implemented according to the followingsteps of firstly, collecting electroencephalogram signals, and preprocessing the obtained electroencephalogram signals; mEMD decomposition being carried out on the preprocessed electroencephalogram signals to obtain more concentrated frequency band signals; screening effective frequency band signals from the obtained frequency band signals according to the maximum mutual information coefficient; reconstructing a component, and performing feature extraction on the reconstructed signal by using fuzzy entropy to form a feature matrix; dividing the data set of the electroencephalogram signals intoa training set and a test set, wherein the training set is used for training a model of the BP neural network; and inputting the obtained feature matrix of the fuzzy entropy into a trained BP neuralnetwork model, and outputting a classification result. The method solves problems that in the prior art, an artificial neural network is low in convergence speed, sensitive in initial weight, prone tofalling into local optimum and poor in global search capacity.

Description

technical field [0001] The invention belongs to the technical field of intelligent detection of physiological information, and relates to an EEG signal classification method of artificial bee colony optimization BP neural network. Background technique [0002] The brain-computer interface is a communication and control system that does not depend on the normal output channels of peripheral nerves and muscles. It directly communicates between brain consciousness and people by analyzing and processing the sent computer information through specific sensors and computers. Control of external devices. It usually provides a new rehabilitation treatment method for patients with nerve damage or muscle damage, so that they do not need to rely on others to complete their own rehabilitation training; with the development of BCI technology, paralyzed patients can be produced by relying on brain-computer interface technology Advanced electronic devices: such as neuroprosthetics, robotic...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a kind of EEG signal classification method of artificial bee colony optimization BP neural network, which solves the problems i

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