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

作者: Rifai Chai , Yvonne Tran , Ashley Craig , Sai Ho Ling , Hung T. Nguyen

DOI: 10.1109/EMBC.2014.6943846

关键词:

摘要: A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines classification between and alert states an autoregressive (AR) model-based power spectral density (PSD) as features extraction method fuzzy particle swarm optimization with cross mutated artificial neural network (FPSOCM-ANN) method. Using 32-EEG channels, results indicated improved overall specificity from 76.99% to 82.02%, sensitivity 74.92 78.99% accuracy 75.95% 80.51% when compared previous studies. The fewer EEG eleven frontal sites resulted in 77.52% for specificity, 73.78% 75.65% being achieved. For ergonomic reasons, configuration channels will capacity monitor there is less set-up time required.

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