Motor imagery electroencephalogram signal classification method based on deep learning

Abstract

The invention discloses a motor imagery electroencephalogram signal classification method based on deep learning, and belongs to the technical field of mode recognition and brain-computer interfaces.The method comprises the following steps: analyzing and preprocessing electroencephalogram signals, carrying out two-dimensional conversion on the electroencephalogram signals, and constructing a convolutional neural model of motor imagery electroencephalogram signal classification; and training the neural network to obtain a motor imagery electroencephalogram signal classification model, and carrying out test and performance evaluation on the model. Compared with the prior art, the method has the advantages that the data of each time point are used as samples, 2D conversion and deep learningmethods are carried out, the classification accuracy of the motor imagery electroencephalogram signals is improved, and meanwhile the classification instantaneity and stability are improved.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and brain-computer interface, and specifically relates to a method for classifying motor imagery EEG signals based on deep learning. Background technique [0002] Brain-Computer Interface (BCI) is an emerging technology that establishes a communication bridge between the brain and external devices, enabling external devices to directly use signals in the brain to guide external activities, such as controlling prosthetics, Movement of electric wheelchairs, etc. Brain-computer interface technology has a wide range of applications, such as smart home, smart medical care, smart entertainment and other fields. The brain-computer interface based on motor imagery EEG signals is the main application of this technology. By analyzing the EEG signals of the brain performing imaginary movements, the brain state and activities can be identified to achieve the purpose of controlling external device...

AI Technical Summary

Problems solved by technology

[0004] Traditional EEG signal classification methods mainly use power spectrum, wavelet transform and other methods to extract features combined with support vector machines and artificial neural networks to classify the extracted features, but the requirements for EEG signal preprocessing are relatively high, and the classification accuracy rate is relatively high. Not tall enough, slower

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