A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition.

Fatigue driving is one of the potential factors threatening road safety, and monitoring drivers' mental state through electroencephalography (EEG) can effectively prevent such risks. In this paper, a new model, DE-GFRJMCMC, is proposed for selecting critical channels and optimal feature subsets from EEG data to improve the accuracy of fatigue driving recognition. The model is validated on the SEED-VIG dataset. The model first selects critical EEG channels using the Differential Evolution (DE) algorithm, extracting important electrode channel information to enhance recognition accuracy. These electrode channels are used to construct a Functional Brain Network (FBN), from which the topological feature set is extracted. Empirical Mode Decomposition (EMD) is then applied to extract the intrinsic mode components as network nodes, thereby reducing the influence of the number of electrode channels on the brain functional network. The topological features extracted from these components form the suboptimal feature set. To minimize redundant information, we propose an improved Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm for selecting the optimal feature subset, ensuring both the efficiency and accuracy of fatigue recognition. The optimal feature subsets were input into various classifiers, and the results showed that the K-Nearest Neighbor (KNN)-based classifier achieved the highest recognition accuracy of 96.11% ± 0.43%, demonstrating the method's stability and robustness. Compared to similar studies, this model shows superior performance in fatigue driving recognition, which is of significant value for research on fatigue driving detection and prevention.