Drowsiness Detection using Minimum Discriminated Features and Single EEG Channel

المؤلفون

  • Raed Mohammed Hussein
  • Firas Sabar Miften
  • Loay E. George

DOI:

https://doi.org/10.32792/jeps.v13i1.242

الكلمات المفتاحية:

Drowsy driving detection، Electroencephalography، wavelet transform.

الملخص

Driver drowsiness is one of the top causes of road fatalities and transportation industry risks.
Electroencephalography (EEG) has been regarded as one of the most reliable physiological signs for
detecting drowsiness in drivers since it directly assesses neurophysiological brain activity. If possible, a
system that monitors driver drowsiness and warnings can prevent the greatest number of traffic incidents.
This study presents a simple and inexpensive method for detecting driver drowsiness or sleep onset with
single-channel EEG signal processing. The primary contribution of this study is the identification of
sleepiness detection from a publicly available graph signal dataset using only two discriminated features
(standard deviation and entropy) and a filter that is easily implementable in any microcontroller device or
smartphone. This study employed the least square support vector machine to categorize driver status into
two groups (awake or drowsy). The proposed method can be implemented in real-time with high
efficiency and precision. Furthermore, this method can be readily applied on a smartphone to create and
expand a sleepiness detection and alert system for vehicle drivers. The trial findings indicate an accuracy
of 95.5 percent.

المراجع

L. Wright, A. Brown, and A. Davidson-Mundt, “Prevalence of Motor Vehicle Crashes Involving

Drowsy Drivers,” J. Pediatr. Nurs., vol. 7, no. 1, pp. 26–42, 1992.

A. Sahayadhas, K. Sundaraj, and M. Murugappan, “Detecting driver drowsiness based on sensors:

A review,” Sensors (Switzerland), vol. 12, no. 12, pp. 16937–16953, 2012, doi:

3390/s121216937.

M. Zhu, J. Chen, H. Li, F. Liang, L. Han, and Z. Zhang, “Vehicle driver drowsiness detection

method using wearable EEG based on convolution neural network,” Neural Comput. Appl., vol.

, no. 20, pp. 13965–13980, 2021, doi: 10.1007/s00521-021-06038-y.

C. S. Wei, Y. Te Wang, C. T. Lin, and T. P. Jung, “Toward Drowsiness Detection Using Non-hair-

Bearing EEG-Based Brain-Computer Interfaces,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 26,

no. 2, pp. 400–406, 2018, doi: 10.1109/TNSRE.2018.2790359.

M. Ogino and Y. Mitsukura, “Portable drowsiness detection through use of a prefrontal singlechannel

electroencephalogram,” Sensors (Switzerland), vol. 18, no. 12, pp. 1–19, 2018, doi:

3390/s18124477.

C. Zhang, L. Sun, F. Cong, T. Kujala, T. Ristaniemi, and T. Parviainen, “Optimal imaging of

multi-channel EEG features based on a novel clustering technique for driver fatigue detection,”

Biomed. Signal Process. Control, vol. 62, no. July, p. 102103, 2020, doi:

1016/j.bspc.2020.102103.

M. Shen, B. Zou, X. Li, Y. Zheng, L. Li, and L. Zhang, “Multi-source signal alignment and

efficient multi-dimensional feature classification in the application of EEG-based subjectindependent

drowsiness detection,” Biomed. Signal Process. Control, vol. 70, no. August, 2021,

doi: 10.1016/j.bspc.2021.103023.

M. Shen, B. Zou, X. Li, Y. Zheng, L. Li, and L. Zhang, “Multi-source signal alignment and

efficient multi-dimensional feature classification in the application of EEG-based subjectindependent

drowsiness detection,” Biomed. Signal Process. Control, vol. 70, no. November 2020,

, doi: 10.1016/j.bspc.2021.103023.

H. Kuang and J. Qu, “LSTM Model with Self-Attention Mechanism for EEG Based Cross-Subject

Fatigue Detection,” in 2021 IEEE 3rd International Conference on Frontiers Technology of

Information and Computer (ICFTIC), 2021, pp. 148–153, doi:

1109/ICFTIC54370.2021.9647146.

H. Wang, L. Xu, A. Bezerianos, C. Chen, and Z. Zhang, “Linking Attention-Based Multiscale

CNN With Dynamical GCN for Driving Fatigue Detection,” IEEE Trans. Instrum. Meas., vol. 70,

pp. 1–11, 2021, doi: 10.1109/TIM.2020.3047502.

S. Sharma, S. K. Khare, V. Bajaj, and I. A. Ansari, “Improving the separability of drowsiness and

alert EEG signals using analytic form of wavelet transform,” Appl. Acoust., vol. 181, p. 108164,

, doi: 10.1016/j.apacoust.2021.108164.

V. Bajaj, S. Taran, S. K. Khare, and A. Sengur, “Feature extraction method for classification of

alertness and drowsiness states EEG signals,” 2020, [Online]. Available:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-

&doi=10.1016%2Fj.apacoust.2020.107224&partnerID=40&md5=b581746ac3d8c56

b1744037effa3f815.

S. K. Khare and V. Bajaj, “Entropy-Based Drowsiness Detection Using Adaptive Variational

Mode Decomposition,” IEEE Sens. J., vol. 21, no. 5, pp. 6421–6428, 2021, doi:

1109/JSEN.2020.3038440.

I. Stancin, N. Frid, M. Cifrek, and A. Jovic, “EEG Signal Multichannel Frequency-Domain Ratio

Indices for Drowsiness Detection Based on Multicriteria Optimization.,” Sensors (Basel)., vol. 21,

no. 20, Oct. 2021, doi: 10.3390/s21206932.

P. Krishnan, S. Yaacob, A. P. Krishnan, M. Rizon, and C. K. Ang, “EEG based drowsiness

detection using relative band power and short-time fourier transform,” 2020, [Online]. Available:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-

&doi=10.2991%2Fjrnal.k.200909.001&partnerID=40&md5=da9a5c55c0b296e995a

f4111622ae.

S. Taran and V. Bajaj, “Drowsiness Detection using Instantaneous Frequency based Rhythms

Separation for EEG Signals,” 2018 Conf. Inf. Commun. Technol. CICT 2018, pp. 1–6, 2018, doi:

1109/INFOCOMTECH.2018.8722429.

P. Krishnan and S. Yaacob, “Drowsiness detection using band power and log energy entropy

features based on EEG signals,” 2019, [Online]. Available:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-

&doi=10.35940%2Fijitee.J9025.0881019&partnerID=40&md5=b5eaf129db24b76f0a349d1a24ec39c5.

O. Rahma and A. Rahmatillah, “Drowsiness analysis using common spatial pattern and extreme

learning machine based on electroencephalogram signal,” J. Med. Signals Sens., vol. 9, no. 2, pp.

–136, 2019, doi: 10.4103/jmss.JMSS_54_18.

C. Zhou, B. Li, S. Li, and K. Huang, “Detection of Vigilance in L3 Autonomous Driving Based on

EEG,” in 2021 IEEE International Conference on Unmanned Systems (ICUS), 2021, pp. 359–365,

التنزيلات

منشور

2023-04-05

إصدار

مجلد 13 عدد 1 (2023): Journal of Education for Pure Science- University of Thi-Qar

القسم

Articles

الرخصة

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