Vigilance lapse identification using sparse EEG electrode arrays

Mental vigilance monitoring is useful in helping people improve their prowess in situations such as sports, musical performance or occupational demands. Electroencephalogram (EEG) signals provide appropriate information for identification of vigilance lapses; however, almost all of the previous EEG-based vigilance-monitoring studies require high-density EEG montages, with the added inconvenience of conductive gels and the requirement for accurate placement of electrodes. In this paper we propose a practical machine learning approach for identification of vigilance lapses using EEG signals recorded from a very sparse electrode configuration with only 4 electrodes: 2 electrodes at the forehead and 2 electrodes behind the ears. This is a challenging problem since these four electrodes are easily contaminated by eye blinks and muscle artifacts. The performance of the proposed machine-learning based algorithm is demonstrated in a real world scenario where vigilance lapses are identified with about 95% accuracy.