538
Views
7
CrossRef citations to date
0
Altmetric
Articles

Deep Review of Machine Learning Techniques on Detection of Drowsiness Using EEG Signal

ORCID Icon, &

References

  • R. O. Phillips, G. Kecklund, A. Anund, and M. Sallinen, “Fatigue in transport: a review of exposure, risks, checks and controls,” Transport Reviews, Vol. 37, no. 6, pp. 742–766, 2017.
  • E. Q. Wu, M. Zhou, D. Hu, L. Zhu, Z. Tang, X. Y. Qiu, P. Y. Deng, L. M. Zhu, and H. Ren, “Self-paced dynamic infinite mixture model for fatigue evaluation of pilots’ brains,” IEEE Transactions on Cybernetics, doi:10.1109/TCYB.2020.3033005.
  • D. Chen, Z. Ma, B. C. Li, Z. Yan, and W. Li, “Drowsiness detection with electrooculography signal using a system dynamics approach,” J. Dyn. Syst. Meas. Contr., Vol. 139, no. 8, pp.1368–1374, Aug. 2017.
  • F. Abtahi, A. Anund, C. Fors, F. Seoane, and K. Lindecrantz. “Association of drivers’ sleepiness with heart rate variability: A pilot study with drivers on real roads,” in EMBEC & NBC 2017. Springer, pp. 149–152, Jun. 2017.
  • M. Awais, N. Badruddin, and M. Drieberg, “A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability,” Sensors, Vol. 17, no. 9, pp. 1–16, Aug. 2017.
  • W.-L. Zheng, and B.-L. Lu, “A multimodal approach to estimating vigilance using eeg and forehead eog,” J. Neural Eng., Vol. 14, no. 2, pp. 026017–026031, Feb. 2017.
  • D. Malathi, J. Dorathi Jayaseeli, S. Madhuri, and K. Senthilkumar, “Electrodermal activity based wearable device for drowsy drivers,” National Conference on Mathematical Techniques and its Applications, Journal of Physics: Conference Series, Vol. 1000, no. 10.1088, pp. 1742–6596, Jan. 2018.
  • D. Artanto, M. P. Sulistyanto, I. D. Pranowo, and E. E. Pramesta, “Drowsiness detection system based on eye-closure using a low-cost emg and esp8266,” in 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), IEEE, Nov. 2017, pp. 235–238.
  • M. V. Yeo, X. Li, K. Shen, and E. P. Wilder-Smith, “Can svm be used for automatic eeg detection of drowsiness during car driving?,” Saf. Sci., Vol. 47, no. 1, pp. 115–124, Jan. 2009.
  • S. K. Lal, and A. Craig, “A critical review of the psychophysiology of driver fatigue,” Biol. Psychol., Vol. 55, no. 3, pp. 173–194, Feb. 2001.
  • G. Kecklund, and T. Akerstedt, “Sleepiness in long distance truck driving: an ambulatory eeg study of night driving,” Ergonomics, Vol. 36, no. 9, pp. 1007–1017, Jul. 1993.
  • H. J. Eoh, M. K. Chung, and S.-H. Kim, “Electroencephalographic study of drowsiness in simulated driving with sleep deprivation,” Int. J. Ind. Ergon., Vol. 35, no. 4, pp. 307–320, Apr. 2005.
  • S. N. Resalat, and V. Saba, “A practical method for driver sleepiness detection by processing the eeg signals stimulated with external flickering light,” Signal. Image. Video. Process., Vol. 9, no. 8, pp. 1751–1757, Feb. 2015.
  • E. Q. Wu, D. Hu, P. Y. Deng, Z. Tang, Y. Cao, W. M. Zhang, L. M. Zhu, and H. Ren, “Nonparametric Bayesian prior inducing deep network for automatic detection of cognitive status,” IEEE Transactions on Cybernetics, 10.1109/TCYB.2020.2977267.
  • F. Bryn. “EEG Pocket Guide”, https://imotions.com/blog/eeg/.
  • H. Shabani, M. Mikaili, and S. M. R. Noori, “Assessment of recurrence quantification analysis (rqa) of eeg for development of a novel drowsiness detection system,” Biomed. Eng. Lett., Vol. 6, no. 3, pp. 196–204, Aug. 2016.
  • A. Jalilifard, and E. B. Pizzolato, “An efficient k-nn approach for automatic drowsiness detection using singlechannel eeg recording,” in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Aug. 2016, pp. 820–824.
  • C.-T. Lin, C.-J. Chang, B.-S. Lin, S. H. Hung, C.-F. Chao, and I.-J. Wang, “A real-time wireless brain–computer interface system for drowsiness detection,” IEEE Trans. Biomed. Circuits Syst., Vol. 4, no. 4, pp. 214–222, Apr. 2010.
  • G. Li, and W.-Y. Chung, “A contextaware eeg headset system for early detection of driver drowsiness,” Sensors, Vol. 15, no. 8, pp. 20873–20893, Aug. 2015.
  • L.-l. Chen, Y. Zhao, J. Zhang, and J. z. Zou, “Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning,” Expert. Syst. Appl., Vol. 42, no. 21, pp. 7344–7355, Nov. 2015.
  • R. N. Khushaba, S. Kodagoda, S. Lal, and G. Dissanayake, “Uncorrelated fuzzy neighborhood preserving analysis based feature projection for driver drowsiness recognition,” Fuzzy Sets Syst., Vol. 221, pp. 90–111, Jun. 2013.
  • M. Akin, M. B. Kurt, N. Sezgin, and M. Bayram, “Estimating vigilance level by using eeg and emg signals,” Neural Computing and Applications, Vol. 17, no. 3, pp. 227–236, May 2008.
  • R. N. Khushaba, and A. Al-Jumaily, “Fuzzy wavelet packet based feature extraction method for multifunction myoelectric control,” Int. J. Biomed. Sci. Vol. 2, no. 1, pp. 186–194, June 2007.
  • E. Q. Wu, L. Zhu, W. Zhang, P. Deng, B. Jia, S. Chen, H. Ren, and G. Zhou, “Novel nonlinear approach for real-time fatigue EEG data: An infinitely warped model of Weighted permutation entropy,” IEEE Trans. Intell. Transp. Syst., Vol. 21, no. 6, pp. 2437–2448, Aug. 2019.
  • E. Q. Wu, L. M. Zhu, G. J. Li, H. J. Li, Z. Tang, R. Hu, and G. R. Zhou, “Nonparametric Hierarchical Hidden semi-Markov model for brain fatigue behavior detection of Pilots during flight,” IEEE Trans. Intell. Transp. Syst., doi:10.1109/TITS.2021.3052801.
  • F. Rohit, V. Kulathumani, R. Kavi, I. Elwarfalli, V. Kecojevic, and A. Nimbarte, “Real-time drowsiness detection using wearable, lightweight brain sensing headbands,” IET Intel. Transport Syst., Vol. 11, no. 5, pp. 255–263, Jun. 2017.
  • I. Dey, S. Jagga, A. Prasad, A. Sharmila, S. K. Borah, and G. Rao. “Automatic detection of drowsiness in eeg records based on time analysis,” in 2017 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, pp. 1–5, Apr. 2017.
  • L.-W. Ko, W.-K. Lai, W.-G. Liang, C.-H. Chuang, S.-W. Lu, Y.-C. Lu, T.-Y. Hsiung, H.-H. Wu, and C.-T. Lin, “Single channel wireless eeg device for realtime fatigue level detection,” in 2015 International Joint Conference on Neural Networks (IJCNN), IEEE, Sep. 2015, pp. 1–5.
  • A. G. Correa, L. Orosco, and E. Laciar, “Automatic detection of drowsiness in eeg records based on multimodal analysis,” Med. Eng. Phys., Vol. 36, no. 2, pp. 244–249, Feb. 2014.
  • R. K. Tripathy, and U. R. Acharya, “Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework,” Biocybernet. Biomed. Eng., Vol. 38, no. 4, pp. 890–902, Jan. 2018.
  • U. Budak, V. Bajaj, Y. Akbulut, O. Atila, and A. Sengur, “An effective hybrid model for eeg-based drowsiness detection,” IEEE Sensors J., Vol. 19, no. 17, pp. 7624–7631, May 2019.
  • J. Park, L. Xu, V. Sridhar, M. Chi, and G. Cauwenberghs, “Wireless dry eeg for drowsiness detection,” in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Sep. 2011, pp. 3298–3301.
  • Y. Punsawad, S. Aempedchr, Y. Wongsawat, and M. Panichkun, “Weighted frequency index for eeg-based mental fatigue alarm system,” Int. J. Appl., Vol. 4, no. 1, pp. 36–41, Apr. 2011.
  • L. Susmitha, S. T. George, M. Subathra, and N. M. Kumar. “Application of multi-domain fusion methods for detecting epilepsy from electroencephalogram using classification methods,” Cognitive Informatics and Soft Computing. Springer,, Aug. 2019. pp. 743–754.
  • M. D’Alessandro, R. Esteller, G. Vachtsevanos, A. Hinson, J. Echauz, and B. Litt, “Epileptic seizure prediction using hybrid feature selection over multiple intracranial eeg electrode contacts: a report of four patients,” IEEE Trans. Biomed. Eng., Vol. 50, no. 5, pp. 603–615, May 2003.
  • B. Kemp, A. H. Zwinderman, B. Tuk, H. A. Kamphuisen, and J. J. Oberye, “Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the eeg,” IEEE Trans. Biomed. Eng., Vol. 47, no. 9, pp. 1185–1194, Sep. 2000.
  • S. C. C. O. T. J. S. O. S. R. S. JSSR, T. Hori, Y. Sugita, E. Koga, S. Shirakawa, K. Inoue, S. Uchida, H. Kuwahara, M. Kousaka, T. Kobayashi, et al., “Proposed supplements and amendments to ‘a manual of standardized terminology, techniques and scoring system for sleep stages of human subjects’, the Rechtschaffen & Kales (1968) standard,” Psychiatry Clin.The Journal of molecular diagnosticshe Journal of Physical Chemistry C, Vol. 55, no. 3, pp. 305–310, Jun. 2001.
  • A. Kandaswamy, V. Krishnaveni, S. Jayaraman, N. Malmurugan, and K. Ramadoss, “Removal of ocular artifacts from eeg—a survey,” IETE. J. Res., Vol. 51, no. 2, pp. 121–130, Mar. 2005.
  • M. K. Islam, A. Rastegarnia, and Z. Yang, “Methods for artifact detection and removal from scalp eeg: A review,” Neurophysiologie Clinique/Clinical Neurophysiology, Vol. 46, no. 4-5, pp. 287–305, Nov. 2016.
  • M. X. Cohen. Analyzing neural time series data: theory and practice. Cambridge: MIT Press, Jan. 2014.
  • A. J. Jerri, “The Shannon sampling theorem—its various extensions and applications: A tutorial review,” Proc. IEEE, Vol. 65, no. 11, pp. 1565–1596, Nov. 1977.
  • S. Scikitlearn. Available: https://scikit-learn.org/stable/modules/preprocessing.html, accessed Aug. 2019.
  • D. H. Wolpert, “The lack of a priori distinctions between learning algorithms,” Neural Comput., Vol. 8, no. 7, pp. 1341–1390, Apr. 1996.
  • D. H. Wolpert, and W. G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput., Vol. 1, no. 1, pp. 67–82, Apr. 1997.
  • V. Bajaj, S. Taran, S. K. Khare, and A. Sengur, “Feature extraction method for classification of alertness and drowsiness states eeg signals,” Appl. Acoust., Vol. 163, pp. 107224–107229, Jun. 2020.
  • C. Anitha, “Detection and analysis of drowsiness in human beings using multimodal signals,” Digital Business. Springer, Vol. 21, 157–174, Jul. 2019.
  • I. Belakhdar, W. Kaaniche, R. Djemal, and B. Ouni, “Single-channel-based automatic drowsiness detection architecture with a reduced number of eeg features,” Microprocess. Microsyst., Vol. 58, pp. 13–23, Apr. 2018.
  • T. L. da Silveira, A. J. Kozakevicius, and C. R. Rodrigues, “Automated drowsiness detection through wavelet packet analysis of a single eeg channel,” Expert. Syst. Appl., Vol. 55, pp. 559–565, Aug. 2016.
  • A. G. Correa, and E. L. Leber. “An automatic detector of drowsiness based on spectral analysis and wavelet decomposition of eeg records,” 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE x Nov. 2010., pp. 1405–1408.
  • D. Merlin Praveena, D. Angelin Sarah, and S. Thomas George, “Deep learning techniques for eeg signal applications–a review,” IETE. J. Res., Vol. 66, pp. 1–8, Apr. 2020.
  • S. Mousavi, F. Afghah, and U. R. Acharya, “Sleepeegnet: automated sleep stage scoring with sequence to sequence deep learning approach,” PloS one, Vol. 14, no. 5, pp. e0216456–e0216470, May 2019.
  • M. L¨angkvist, L. Karlsson, and A. Loutfi, “Sleep stage classification using unsupervised feature learning,” Advances in Artificial Neural Systems, Vol. 2012, pp. 1–9, Jul. 2012.
  • N. Kulkarni, and V. Bairagi, “Extracting salient features for eeg-based diagnosis of Alzheimer’s disease using support vector machine classifier,” IETE. J. Res., Vol. 63, no. 1, pp. 11–22, Oct. 2017.
  • B. Hjorth, “Eeg analysis based on time domain properties,” Electroencephalogr. Clin. Neurophysiol., Vol. 29, no. 3, pp. 306–310, Sep. 1970.
  • V. Phanikrishna B, and S. chinara. “Time domain parameters as a feature for single-channel eeg-based drowsiness detection method,” in 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, pp. 1–5, May 2020.
  • S.-F. Liang, C.-E. Kuo, Y.-H. Hu, and Y.-S. Cheng, “A rule-based automatic sleep staging method,” J. Neurosci. Methods, Vol. 205, no. 1, pp. 169–176, Mar. 2012.
  • Matlab. “Peak-analysis”, Available: https://in.mathworks.com/help/signal/examples/peak-analysis.html, accessed Mar. 2020.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.