Classification of Driver Fatigue in an Electroencephalography-Based Countermeasure System with Source Separation Module

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2015 (EMBC 2015), 2015, pp. 514 - 517
Issue Date:
2015-08-25
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An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05).
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