Driver Fatigue Classification With I ndependent Component by Entropy Rate Bound Minimization Analysis i n an EEG-Based System

Abstract-In the first place, single processing project presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. in the same way, the system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation,autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method couldbe utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.