Motor imagery electroencephalogram data classification method and system

Abstract

The invention relates to a motor imagery electroencephalogram data classification method which comprises the following steps: acquiring motor imagery electroencephalogram signals generated when a subject executes a motor imagery task to obtain original electroencephalogram data; performing data preprocessing on the original electroencephalogram data to obtain preprocessed data; dividing the preprocessed data into a training set and a test set; establishing an inception deep neural network model, and inputting the training set into the inception deep neural network model for training to obtaina trained model; and testing the trained model by utilizing the verification set, judging whether the trained model is optimal on the verification set or not, and if so, inputting the data of the testset into the optimal model to obtain a classification result. According to the method, the inception module and the convolutional neural network are combined, the inception deep neural network modelis established, calculation resources can be efficiently utilized, the calculation complexity is reduced, and the classification effect of the neural network is improved.

Description

technical field [0001] The invention relates to the technical field of brain-computer interface, in particular to a method and system for classifying motor imagery EEG data. Background technique [0002] Most of the current methods for classifying motor imagery EEG signals are based on manually extracted features. Since the manually extracted EEG features are often redundant, a series of extracted features can be regarded as a sparse matrix. How to effectively use the effective information in the sparse feature matrix is ​​a problem that needs to be solved. The current deep learning network has a trend of getting deeper and deeper, that is, the number of layers of the network is increasing. Although increasing the number of network layers can improve the accuracy rate, the number of parameters contained in the network also increases exponentially, which will cost more computing costs and make the network more difficult to converge. This patent uses the inception module to ...

AI Technical Summary

Problems solved by technology

[0005] Based on this, it is necessary to provide a motor imagery EEG data classification method and system for the problem that the manually extracted features are not conducive to the effective EEG signal classification of the classifier and the increase in the number of deep learning network layers causes the classification effect to deteriorate.

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