Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system

Rifai Chai, Yvonne Tran, Ashley Craig, Sai Ho Ling, Hung T. Nguyen
2014 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
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
more » ... 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.
doi:10.1109/embc.2014.6943846 pmid:25570210 dblp:conf/embc/ChaiTCLN14 fatcat:vmeremsdn5bcfbbbvl3oqh45ta