Application of genetic algorithm based support vector machine in selection of new EEG rhythms for drowsiness detection

作者：

Highlights：

• GA-SVM is used to find the optimal rhythms for drowsiness detection.

• The original EEG signals are decomposed using wavelet packet transform.

• The drowsiness detection effect of the new rhythm was evaluated using LOSO-CV.

摘要

•GA-SVM is used to find the optimal rhythms for drowsiness detection.•The original EEG signals are decomposed using wavelet packet transform.•The drowsiness detection effect of the new rhythm was evaluated using LOSO-CV.

论文关键词：Electroencephalogram (EEG),Wavelet packet transform (WPT),Drowsiness detection Cross-validation (CV),Genetic algorithm (GA),Support vector machines (SVM)

论文评审过程：Received 10 September 2019, Revised 14 January 2021, Accepted 16 January 2021, Available online 23 January 2021, Version of Record 31 January 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114634