An algorithm for automatic detection of drowsiness for use in wearable EEG systems.

Lack of proper restorative sleep can induce sleepiness at odd hours making a person drowsy. This onset of drowsiness can be detrimental for the individual in a number of ways if it happens at an unwanted time. For example, drowsiness while driving a vehicle or operating heavy machinery poses a threat to the safety and wellbeing of individuals as well as those around them. Timely detection of drowsiness can prevent the occurrence of unfortunate accidents thereby improving road and work environment safety. In this paper, by analyzing the electroencephalographic (EEG) signals of human subjects in the frequency domain, several features across different EEG channels are explored. Of these, three features are identified to have a strong correlation with drowsiness. A weighted sum of these defining features, extracted from a single EEG channel, is then used with a simple classifier to automatically separate the state of wakefulness from drowsiness. The proposed algorithm resulted in drowsiness detection sensitivity of 85% and specificity of 93%.