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Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System.
- Source :
-
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2017 May; Vol. 21 (3), pp. 715-724. Date of Electronic Publication: 2016 Feb 19. - Publication Year :
- 2017
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 2168-2208
- Volume :
- 21
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- IEEE journal of biomedical and health informatics
- Publication Type :
- Academic Journal
- Accession number :
- 26915141
- Full Text :
- https://doi.org/10.1109/JBHI.2016.2532354