生物医学工程研究2018，Vol.37Issue(2)：127-131,5.DOI:10.19529/j.cnki.1672-6278.2018.02.02
基于遗传算法的运动想象脑电信号分类准确率的提升方法
An improved classification method for motor imagery EEG signals based on genetic algorithm
摘要
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.关键词
脑电信号(EEG)/共同空间模式(CSP)/遗传算法(GA)/分类结果加权/数据筛选Key words
Electroencephalogram(EEG)/Common spatial pattern(CSP)/Genetic algorithm(GA)/Classification result weigh-ting/Data screening分类
医药卫生引用本文复制引用
高诺,鲁昊,鲁守银,吴林彦..基于遗传算法的运动想象脑电信号分类准确率的提升方法[J].生物医学工程研究,2018,37(2):127-131,5.基金项目
国家自然科学基金资助项目(61403237):山东省科技重大专项(2015ZDXXX0801A03) （61403237）
山东省重点研发计划项目(2017CXGC1505). （2017CXGC1505）
