Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System

• Published in: IEEE journal of biomedical and health informatics Vol. 21; no. 3; pp. 715 - 724
• Main Authors: Rifai Chai, Naik, Ganesh R., Tuan Nghia Nguyen, Sai Ho Ling, Tran, Yvonne, Craig, Ashley, Nguyen, Hung T.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Algorithms; Automobile Driving; Autoregressive model; Autoregressive models; Autoregressive processes; Bayes Theorem; Bayesian analysis; bayesian neural network; Classification; Classification algorithms; Driver fatigue; EEG; Electroencephalography; electroencephalography (EEG); Electroencephalography - methods; Entropy; entropy rate bound minimization; Fatigue; Fatigue - classification; Fatigue - diagnosis; Fatigue - physiopathology; Feature extraction; Humans; independent-component analysis (ICA); Middle Aged; Neural networks; Optimization; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Source separation; Traffic accidents & safety; Vehicles; Young Adult
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2016.2532354

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.