Title: Joint time-frequency analysis of EEG for the drowsiness detection: a study of cognitive behavioural patterns of the brain
Authors: Dabbu Suman; Mudigonda Malini; B. Ramreddy
Addresses: Department of Biomedical Engineering, University College of Engineering (A), Osmania University, Hyderabad 500007, India ' Department of Biomedical Engineering, University College of Engineering (A), Osmania University, Hyderabad 500007, India ' Department of Physiology, Apollo Institute of Medical Sciences, Banjara Hills, Hyderabad 500007, India
Abstract: Drowsiness detection plays a vital role in accidents avoidance systems, thereby saving many precious lives. According to the World Health Organization, drowsiness has been the radical contributor of road fatalities. Electroencephalogram (EEG) is a physiological signal which relays the functioning of brain and is widely used in the diagnosis of neurological disorders. This study extrapolates the EEG signal analysis to examine several cognitive tasks. In this report, the EEG signal is processed to detect the behavioural patterns of the brain and drowsiness state of the drivers while performing monotonous driving for long distances. An eight-channel EEG data acquisition system is used to acquire the EEG data from 13 male volunteers. The EEG signal is pre-processed and decomposed into various rhythms by applying digital filter in MATLAB 2007b (Mathworks, Inc., USA). Time-frequency domain analysis has been done to extract certain features, PSG and PRMSD, which are statistically significant (ρ < 0.05) in the detection of drowsiness. The driving profile is classified into active and drowsy by a threshold, and linear regression analysis has been performed on the features extracted. A drowsiness index is proposed stating a positive correlation (0.8-0.9) between the total mean and the drowsy mean of the subject.
Keywords: drowsiness; PSG; power within spectrogram; PRMSD; power within the root mean square deviation.
International Journal of Vehicle Safety, 2017 Vol.9 No.3, pp.262 - 277
Received: 06 Oct 2016
Accepted: 19 Apr 2017
Published online: 16 Jul 2017 *