Driver Fatigue Classification with Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-based System (original) (raw)
— This paper presents a two-class electroencephalography (EEG)-based classification for classifying of driver fatigue (fatigue state vs. 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 (PSD) 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. Index Terms—electroencephalography (EEG), driver fatigue, autoregressive (AR) model, independent component analysis, entropy rate bound minimization, Bayesian neural network.
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