EEG Brain Connectivity Analysis to Detect Driver Drowsiness Using Coherence

Awais, M. and Badruddin, N. and Drieberg, M. (2018) EEG Brain Connectivity Analysis to Detect Driver Drowsiness Using Coherence. Proceedings - 2017 International Conference on Frontiers of Information Technology, FIT 2017, 2017-J . pp. 110-114.

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Drowsiness at the wheel is one of the major contributing factors towards road accidents. Therefore, efforts have been made to detect driver drowsiness using electroencephalogram (EEG). The use of EEG as a possible driver drowsiness indicator is commonly accepted. However, in this paper, we have studied brain connectivity measure instead of the traditional spectral power measures. For this purpose, the EEG coherence analysis is performed to examine the functional connectivity between various brain regions during the transitional phase, i.e., from alert state to drowsy state. Data collection is performed in a simulator based environment. Twenty-two healthy subjects voluntarily participated in the study after providing their consent. All possible combinations of inter- and intra-hemispheric coherences are analyzed. Because of the unavailability of common gold standard, video recordings are captured during the experiment to mark the drowsy state. To verify the statistical significance of the proposed features, paired t-test is performed. The analysis revealed significant differences (p0.05) in inter- and intra-hemispheric coherences (brain connectivity analysis) between alert and drowsy state, which shows the potential of coherence analysis in detection drowsiness. © 2017 IEEE.

Item Type:Article
Impact Factor:cited By 0; Conference of 15th International Conference on Frontiers of Information Technology, FIT 2017 ; Conference Date: 18 December 2017 Through 20 December 2017; Conference Code:134341
Uncontrolled Keywords:Brain; Coherent light; Highway accidents; Video recording, Brain connectivity; Contributing factor; Drowsiness; Electro-encephalogram (EEG); Functional connectivity; Inter-hemispheric; Intra-hemispheric; Statistical significance, Electroencephalography
Departments / MOR / COE:Research Institutes > Institute for Health Analytics
ID Code:21839
Deposited By: Ahmad Suhairi
Deposited On:01 Aug 2018 01:21
Last Modified:23 Oct 2018 01:42

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