Multi-class motion imagination EEG signal classification method based on characteristic recombination and wavelet transformation

Abstract

The invention discloses a multi-class motion imagination EEG signal classification method based on characteristic recombination and wavelet transformation, mainly solving the problem of less classes and low classification accuracy of present technologies for classifying EEG signals. The method comprise following steps: 1) collecting motion imagination EEG signals and obtaining a training set and a test set; 2) training a two-grade classifier through characteristic combination, wavelet transformation and common space pattern algorithms; 3) extracting test characteristic classification vectors of the test set according to a method corresponding to step 2); 4) by means of the trained classifier, performing signal classification on test signals through the characteristic vectors of the test set to obtain the classifications of EEG signals of imagination left hand motion, imagination right hand motion, imagination feet motion and imagination tongue motion of the test signals. The method of the invention realizes classification on multi-class motion imagination signals and increases classification accuracy, and can be used in intelligent product control of on-line system containing motion imagination brain-computer interface BCI.

Description

technical field [0001] The invention belongs to the field of information technology, and relates to the classification method of the four types of motor imagery EEG signals of the left hand, right hand, foot and tongue, which can be used for medical treatment and life assistance for the severely disabled, such as intelligent wheelchairs, control of mechanical limbs, and can also be used for modern Intelligent products, such as aircraft control, intelligent car driving and other EEG product control with motor imagination brain-computer interface BCI online system. Background technique [0002] Modern neurobiology believes that the cerebral cortex can be divided into several regions according to different characteristics and functions. Different regions are in charge of and regulate different parts of the body to achieve different functions. The functions of various parts of the body have a corresponding relationship in the cerebral cortex, such as The precentral gyrus mainly ...

AI Technical Summary

Problems solved by technology

However, this co-space mode CSP algorithm has the following disadvantages: 1. It can only classify two types of EEG signals, and the traditional CSP method cannot be used to classify four types of signals; 2. The feature extraction process of the CSP algorithm integrates all leads The correlation of each lead signal has not been analyzed and synthesized. In addition, the individual differences of human EEG signals are large, so the accuracy of traditional CSP for some subjects is low.

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