title: Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system.
abstract: A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as the classification method. Using 32-EEG channels, results indicated an improved overall specificity from 76.99% to 82.02%, an improved sensitivity from 74.92 to 78.99% and an improved accuracy from 75.95% to 80.51% when compared to previous studies. The classification using fewer EEG channels, with eleven frontal sites resulted in 77.52% for specificity, 73.78% for sensitivity and 75.65% accuracy being achieved. For ergonomic reasons, the configuration with fewer EEG channels will enhance capacity to monitor fatigue as there is less set-up time required.
authors.forename: Rifai
authors.initials: R
authors.lastname: Chai
authors.forename: Yvonne
authors.initials: Y
authors.lastname: Tran
authors.forename: Ashley
authors.initials: A
authors.lastname: Craig
authors.forename: Sai Ho
authors.initials: SH
authors.lastname: Ling
authors.forename: Hung T
authors.initials: HT
authors.lastname: Nguyen
mesh_terms: D000465:Algorithms; D001334:Automobile Driving; D004569:Electroencephalography; D006339:Heart Rate; D006801:Humans; D005222:Mental Fatigue / Q000145:classification / Q000503:physiopathology*; D016571:Neural Networks, Computer