Motor imagery electroencephalogram signal classification method of semi-supervised learning optimization SVM

Abstract

The invention discloses a motor imagery electroencephalogram signal classification method for semi-supervised learning optimization SVM, and the method comprises the steps: obtaining motor imagery electroencephalogram signals, and carrying out the preprocessing of the motor imagery electroencephalogram signals; dividing the preprocessed motor imagery electroencephalogram signals into a training set, a verification set and a test set, and performing SVM classifier initialization training of undetermined parameters based on the training set to obtain mutual information of the verification set; updating the training set, and performing SVM classifier iterative training based on the updated training set to obtain mutual information of the verification set after each iteration; constructing anobjective function based on the obtained mutual information and the SVM classifier of the undetermined parameters, and obtaining optimal parameters of the SVM classifier by adopting an empire algorithm; and obtaining an average iterative classification rate by adopting the optimized SVM classifier. According to the method, the imperialist competitive algorithm is used for optimizing the SVM, better parameters can be obtained, and then a better classification effect is obtained.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a motor imagery EEG signal classification method for semi-supervised learning optimization SVM. Background technique [0002] In the prior art, motor imagery electroencephalogram (EEG) classification learning methods with labeled samples are roughly divided into two categories: (1) Supervised learning. Supervised learning is the process of training on the basis of a labeled sample set. In supervised learning, the number of labeled samples is very important. Only a model built with a large number of labeled samples can accurately reflect the real distribution of the data, thereby improving the predictive performance of the classifier. (2) Semi-supervised learning. In the actual situation, the marked samples and unmarked samples of motor imagery EEG coexist, and the semi-supervised learning comprehensively considers the use of a small number of marked samples and a large number...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the existing technical problems such as difficult classification of motor imagery EEG signals and poor effect, the present invention provides a semi-supervised learning optimization SVM motor imagery EEG signal classification method, which combines mutual information and cross-validation in semi-supervised learning Construct the objective function, and establish a model to optimize the parameters of SVM, and classify the motor imagery EEG by optimizing the parameters

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