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Sleep onset period detection using slow eyelid movement (SEM) through eye aspect ratio with electroencephalogram (EEG)

Abstract:

This study presents the development of sleep onset period detection using SEM through eye aspect ratio with EEG. The researchers made use of a camera module, Neurosky Mindwave headset and a microcontroller coupled up with an improvised alarm system composed of a buzzer and vibration motors, to detect drowsiness of a subject and to alert the same. Raspberry Pi Camera Module was utilized for eyelid movement detection, Neurosky Mindwave headset for brain wave monitoring and a microcontroller to manage and activate the alarm system of the device. The results of the study showed that the integration of eyelid movement and electroencephalogram provides a more accurate method of determining sleep onset period compared to previous studies. The integrated SEM and EEG parameters provided 97.5% accuracy. This research will greatly benefit the safety of the drivers. Also, this will be beneficial to companies which require its employees to have a high level of alertness as demanded by certain occupations.