title: Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System.
abstract: This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. 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 could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.
authors.forename: Rifai
authors.initials: R
authors.lastname: Chai
authors.forename: Ganesh R
authors.initials: GR
authors.lastname: Naik
authors.forename: Tuan Nghia
authors.initials: TN
authors.lastname: Nguyen
authors.forename: Sai Ho
authors.initials: SH
authors.lastname: Ling
authors.forename: Yvonne
authors.initials: Y
authors.lastname: Tran
authors.forename: Ashley
authors.initials: A
authors.lastname: Craig
authors.forename: Hung T
authors.initials: HT
authors.lastname: Nguyen
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001334:Automobile Driving; D001499:Bayes Theorem; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D005221:Fatigue / Q000145:classification / Q000175:diagnosis / Q000503:physiopathology; D006801:Humans; D008875:Middle Aged; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult