TY  - JOUR
PY  - 2022//
TI  - A new feature analysis approach to selecting channels of EEG for fatigue driving
JO  - Computational and mathematical methods in medicine
A1  - Liao, Yiqi
A1  - Shangguan, Pengpeng
A1  - Peng, Yiran
A1  - Qiu, Taorong
SP  - e4640426
EP  - e4640426
VL  - 2022
IS  - 
N2  - Fatigued driving is a significant contributor to traffic accidents. There are some issues with common EEG data of 32 channels, 64 channels, and 128 channels, such as difficult acquisition, high data redundancy, and difficult practical application. A new channel selection method called ReliefF_SFS is proposed to address the problem of how to reduce the number of channels while maintaining classification accuracy. It combines the ReliefF algorithm and the sequential forward selection (SFS) algorithm. When only T6, O1, Oz, T4, P3, and FC3 are used, the classification accuracy under Theta_Std+FE combined with ReliefF_SFS achieves 99.45%. The strategy suggested in this paper not only ensures the recognition accuracy but also reduces the number of channels when compared to other models based on the same data set.<p />  <p>Language: en</p>
LA  - en
SN  - 1748-670X
UR  - http://dx.doi.org/10.1155/2022/4640426
ID  - ref1
ER  -