C. Conceição, J. Costa, and A.C. Rosa (Portugal)
Drowsiness Detection, Electroencephalogram,Evolutionary Algorithms.
This work presents an effective real time system to detect drowsiness, based on the analysis of electroencephalogram signal bands. It is a low complexity system, with the capability of being personalized for each user and completely independent in the detection process. Tests have been done with pre-recorded MWT data, featuring ten different subjects each with up to four nap attempts, resulting in an average anticipation of two minutes and thirty-four seconds, enabling the implementation of early warning systems. Four real-time tests were performed showing detections when the subjects were instructed to relax. This real time detection method allows a warning to be issued, making it suitable for practical implementation on risk situations. Its low complexity analysis over EEG, along with the anticipation results, present a practical implementation of drowsiness detection device.
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