BibTex format

author = {Patrick, KCA and Imtiaz, SA and Bowyer, S and Rodriguez, Villegas E},
doi = {10.1109/EMBC.2016.7591488},
publisher = {IEEE},
title = {An Algorithm for Automatic Detection of Drowsiness for Use inWearable EEG Systems},
url = {},
year = {2016}

RIS format (EndNote, RefMan)

AB - Lack of proper restorative sleep can induce sleepinessat odd hours making a person drowsy. This onset of drowsinesscan be detrimental for the individual in a number of waysif it happens at an unwanted time. For example, drowsinesswhile driving a vehicle or operating heavy machinery poses athreat to the safety and wellbeing of individuals as well as thosearound them. Timely detection of drowsiness can prevent theoccurrence of unfortunate accidents thereby improving roadand work environment safety. In this paper, by analyzing theelectroencephalographic (EEG) signals of human subjects inthe frequency domain, several features across different EEGchannels are explored. Of these, three features are identified tohave a strong correlation with drowsiness. A weighted sum ofthese defining features, extracted from a single EEG channel,is then used with a simple classifier to automatically separatethe state of wakefulness from drowsiness. The proposed algorithmresulted in drowsiness detection sensitivity of 85% andspecificity of 93%.
AU - Patrick,KCA
AU - Imtiaz,SA
AU - Bowyer,S
AU - Rodriguez,Villegas E
DO - 10.1109/EMBC.2016.7591488
PY - 2016///
SN - 1557-170X
TI - An Algorithm for Automatic Detection of Drowsiness for Use inWearable EEG Systems
UR -
UR -
ER -