Key result
Combined ReliefF and sequential forward selection achieves ~99% fatigued driving accuracy using minimal EEG channels.
Why the study?
Common EEG data with many channels have issues such as difficult acquisition, high data redundancy, and challenging practical application in fatigued driving detection.
The proposed ReliefF_SFS method reduces the number of EEG channels required for fatigue driving detection while maintaining high classification accuracy.
May simplify wearable EEG monitoring for driver fatigue; leaves open prospective validation before clinical adoption.
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.
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Liao et al. (2022) studied Fatigued driving. ReliefF_SFS channel selection method vs. Other models based on the same data set was evaluated on Classification accuracy. A new channel selection method combining ReliefF and sequential forward selection achieved 99.45% classification accuracy for fatigued driving using only six EEG channels.
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