计算机与数字工程2024，Vol.52Issue(1)：195-200,6.DOI:10.3969/j.issn.1672-9722.2024.01.032
基于共空间模式的脑电信号疲劳检测
EEG Fatigue Detection Based on Common Spatial Pattern
刘燕 1郑威 1龙佳伟1
作者信息
- 1. 江苏科技大学电子信息学院 镇江 212000
- 折叠
摘要
Abstract
Because EEG can directly reflect the fatigue state of cerebral cortex,this paper proposes a fatigue detection method based on common spatial pattern.Firstly,the data set is preprocessed by filtering,and then the common spatial pattern is used to ex-tract features.Finally,the effective spatial features are classified by support vector machine.In addition,the experiment also uses 5 fold and 10 fold cross validation method to evaluate the method.It explores the value of correlation coefficient m of EEG fatigue char-acteristic order,divides the brain regions and compares the accuracy of fatigue recognition in each region.The results show that,the recognition rate of this method is higher than that of the methods based on sample entropy and fuzzy entropy,the average fatigue de-tection accuracy rate can reach 98.54%,the whole scalp fatigue recognition rate is the highest,and the frontal fatigue recognition rate is better than other regions,up to 92.54%.This study can provide a more simple and accurate detection method for the develop-ment of fatigue detection equipment,and help to promote the application of wearable brain computer interface in fatigue driving ear-ly warning.关键词
脑电信号/疲劳检测/共空间模式/支持向量机/交叉验证/模糊熵Key words
EEG signal/fatigue detection/common spatial pattern/support vector machine/cross validation/fuzzy entropy分类
信息技术与安全科学引用本文复制引用
刘燕,郑威,龙佳伟..基于共空间模式的脑电信号疲劳检测[J].计算机与数字工程,2024,52(1):195-200,6.
