An improved classification method for motor imagery EEG signals based on genetic algorithm
GAO Nuo
LU Hao
LU Shouyin
WU Linyan
Abstract：In order to improve the recognition rate of motor imagery EEG signals,an improvement classification method based on genetic algorithm (GA) was proposed.The proposed method combined GA and common spatial pattern (CSP) to extract the features of different time.After considering the classification accurate,GA was used to calculate different time slices’ weights.And based on the weights, the data credibility was calculated.Using the EEG signals collected in this laboratory,the accuracy of classification improved from about 80%before weighting to more than 95%after weighting.The experimental results confirm that this method can effectively improve the classification accuracy of EEG signals,and can eliminate low-quality data according to credibility.At the same time, this method can also be combined with other feature extraction methods to calculate the validity and credibility of different time and frequen-cy characteristics to improve the classification accuracy.
Keywords：Electroencephalogram(EEG)Common spatial pattern(CSP)Genetic algorithm(GA)Classification result weigh-tingData screening
Publication Date：2018-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：5( 127-131 )