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Single-Channel Real-Time Drowsiness Detection Based on Electroencephalography.

Publication ,  Conference
Albalawi, H; Li, X
Published in: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
July 2018

The need of a reliable drowsiness detection system is arising today, as drowsiness is considered as a major cause for accidents as much as alcohol. In this paper, we propose a real-time drowsiness detection algorithm based on a single-channel electroencephalography (EEG) for wearable devices without demanding computing and power resources. The proposed algorithm adopts a cumulative counter to extract important features from 8 different frequency bands: delta (1-3 Hz), theta ($\not\subset-7$ Hz), low-alpha (8-9 Hz), high-alpha (10-12 Hz), low-beta (13-17 Hz), high-beta (18-30 Hz), low-gamma (31-40 Hz), and high-gamma (41-50 Hz). These features are then processed by a support vector machine (SVM) to distinguish between drowsy and awake states. Our preliminary results demonstrate that the proposed algorithm is capable of detecting drowsiness with superior accuracy (83.36%) over the conventional method (70.62%).

Duke Scholars

Published In

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

DOI

EISSN

2694-0604

ISSN

2375-7477

Publication Date

July 2018

Volume

2018

Start / End Page

98 / 101

Related Subject Headings

  • Wakefulness
  • Support Vector Machine
  • Sleep Stages
  • Humans
  • Electroencephalography
  • Automobile Driving
  • Algorithms
 

Citation

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MLA
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Albalawi, H., & Li, X. (2018). Single-Channel Real-Time Drowsiness Detection Based on Electroencephalography. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference (Vol. 2018, pp. 98–101). https://doi.org/10.1109/embc.2018.8512205
Albalawi, Hassan, and Xin Li. “Single-Channel Real-Time Drowsiness Detection Based on Electroencephalography.” In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2018:98–101, 2018. https://doi.org/10.1109/embc.2018.8512205.
Albalawi H, Li X. Single-Channel Real-Time Drowsiness Detection Based on Electroencephalography. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference. 2018. p. 98–101.
Albalawi, Hassan, and Xin Li. “Single-Channel Real-Time Drowsiness Detection Based on Electroencephalography.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, vol. 2018, 2018, pp. 98–101. Epmc, doi:10.1109/embc.2018.8512205.
Albalawi H, Li X. Single-Channel Real-Time Drowsiness Detection Based on Electroencephalography. Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference. 2018. p. 98–101.

Published In

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

DOI

EISSN

2694-0604

ISSN

2375-7477

Publication Date

July 2018

Volume

2018

Start / End Page

98 / 101

Related Subject Headings

  • Wakefulness
  • Support Vector Machine
  • Sleep Stages
  • Humans
  • Electroencephalography
  • Automobile Driving
  • Algorithms