TY - JOUR
T1 - Driver Fatigue Classification with Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System
AU - Chai, Rifai
AU - Naik, Ganesh R.
AU - Nguyen, Tuan Nghia
AU - Ling, Sai Ho
AU - Tran, Yvonne
AU - Craig, Ashley
AU - Nguyen, Hung T.
PY - 2017/5
Y1 - 2017/5
N2 - 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.
AB - 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.
KW - Autoregressive model
KW - Bayesian neural network
KW - driver fatigue
KW - electroencephalography (EEG)
KW - entropy rate bound minimization
KW - independent-component analysis (ICA)
UR - http://www.scopus.com/inward/record.url?scp=84987853522&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2016.2532354
DO - 10.1109/JBHI.2016.2532354
M3 - Article
C2 - 26915141
AN - SCOPUS:84987853522
VL - 21
SP - 715
EP - 724
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 3
ER -