AUTOMATIC MICROSLEEP DETECTION BASED ON KNN CLASSIFIER UTILIZING SELECTED AND EFFECTIVE EEG CHANNELS

Md Mahmudul Hasan; Mirza Mahfuj Hossain; Norizam Sulaiman; Md Nahidul Islam; Sayma Khandaker

doi:10.11113/jurnalteknologi.v86.22154

Authors

Md Mahmudul Hasan Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia
Mirza Mahfuj Hossain Department of Computer Science and Engineering, Jashore University of Science and Technology, Jashore-7408, Bangladesh
Norizam Sulaiman Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia
Md Nahidul Islam Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia
Sayma Khandaker Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia

DOI:

https://doi.org/10.11113/jurnalteknologi.v86.22154

Keywords:

Microsleep detection, electroencephalogram signal, channel selection, correlation coefficient, k-nearest neighbor

Abstract

Annually, the global economy suffers significant financial losses due to decreased productivity of work, accidents, and crashes in traffic resulting from microsleep. To reduce the adverse impacts of microsleep, it is necessary to have a discreet, dependable, and socially acceptable method of detecting microsleep episodes consistently throughout the day, every single day. Regrettably, the current solutions fail to match these specified criteria. Moreover, by utilizing sophisticated features and employing machine learning techniques, it is possible to process electroencephalogram (EEG) information in a highly efficient manner, enabling the rapid and successful detection of microsleep. The selection of an optimum channel and the use of a competent classification algorithm are crucial for effective microsleep detection. One unique channel selecting strategy has been introduced in the current study to evaluate the classifying accuracy of microsleep detection based on EEG. This strategy is based on correlation coefficients and utilizes the K-Nearest Neighbor (KNN) method. Furthermore, the Fast Fourier Transform (FFT) was employed for extracting the feature, so validating the endurance of the proposed technique. In order to enhance the speed of the microsleep detecting system, the study was performed using 3 distinct time windows: 0.5s, 0.75s, and 1s. The study revealed that the suggested approach achieved a classification accuracy of 98.28% within a time window of 0.5 seconds to detect microsleep using EEG signal. The exceptional effectiveness of the given system can be efficiently utilized in detecting microsleep using EEG signal.

References

National Safety Council. 2023. Drivers are Falling Asleep Behind the Wheel. Accessed: Dec. 05, 2023. [Online].

Available: https://www.nsc.org/road/safety-topics/fatigued-driver.

O. M. Qureshi, A. Hafeez, and S. S. H. Kazmi. 2020. Ahmedpur Sharqia Oil Tanker Tragedy: Lessons Learnt from One of the Biggest Road Accidents in History. Journal of Loss Prevention in the Process Industries. 67: 104243.

Doi: https://doi.org/10.1016/j.jlp.2020.104243.

M. M. Hasan, M. M. Hossain, N. Sulaiman, and S. Khandaker. 2024. Microsleep Predicting Comparison between LSTM and ANN based on the Analysis of Time Series EEG Signal. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 16(1): 25-31.

Doi: https://doi.org/10.54554/jtec.2024.16.01.004.

R. P. Balandong, R. F. Ahmad, M. N. Mohamad Saad, and A. S. Malik. 2018. A Review on EEG-Based Automatic Sleepiness Detection Systems for Driver. IEEE Access. 6: 22908-22919.

Doi: https://doi.org/10.1109/ACCESS.2018.2811723.

A. B. Buriro, R. Shoorangiz, S. J. Weddell, and R. D. Jones. 2018. Predicting Microsleep States Using EEG Inter-Channel Relationships. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 26(12): 2260-2269.

Doi: https://doi.org/10.1109/TNSRE.2018.2878587.

M. M. Hasan, M. M. Hossain, and N. Sulaiman. 2023. Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal. Applications of Modelling and Simulation. 7: 178-189.

T. Nguyen, S. Ahn, H. Jang, S. C. Jun, and J. G. Kim. 2017. Utilization of a Combined EEG/NIRS System to Predict Driver Drowsiness. Scientific Reports. 7(1): 43933.

Doi: https://doi.org/10.1038/srep43933.

C. Wang et al. 2020. Spectral Analysis of EEG during Microsleep Events Annotated via Driver Monitoring System to Characterize Drowsiness. IEEE Transactions on Aerospace and Electronic Systems. 56(2): 1346-1356.

Doi: https://doi.org/10.1109/TAES.2019.2933960.

H. Laouz, S. Ayad, and L. S. Terrissa. 2020. Literature Review on Driver’s Drowsiness and Fatigue Detection. International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco: IEEE. 1-7.

Doi: https://doi.org/10.1109/ISCV49265.2020.9204306.

B. C. Banz, D. Hersey, and F. E. Vaca. 2021. Coupling Neuroscience and Driving Simulation: A Systematic Review of Studies on Crash-risk Behaviors in Young Drivers. Traffic Injury Prevention. 22(1): 90-95.

Doi: https://doi.org/10.1080/15389588.2020.1847283.

A. Balaji, U. Tripathi, V. Chamola, A. Benslimane, and M. Guizani. 2023. Toward Safer Vehicular Transit: Implementing Deep Learning on Single Channel EEG Systems for Microsleep Detection. IEEE Transactions on Intelligent Transportation Systems. 24(1): 1052-1061.

Doi: https://doi.org/10.1109/TITS.2021.3125126.

C. Vidaurre, N. Krämer, B. Blankertz, and A. Schlögl. 2009. Time Domain Parameters as a feature for EEG-based Brain–Computer Interfaces. Neural Networks. 22(9): 1313-1319.

Doi: https://doi.org/10.1016/j.neunet.2009.07.020.

D. P. Subha, P. K. Joseph, R. Acharya U, and C. M. Lim. 2010. EEG Signal Analysis: A Survey. Journal of Medical Systems. 34(2): 195-212.

Doi: https://doi.org/10.1007/s10916-008-9231-z.

C. Dussault, J.-C. Jouanin, and C.-Y. Guezennec. 2004. EEG and ECG Changes during Selected Flight Sequences. Aviation, Space, and Environmental Medicine. 75(10): 889-897.

C. Papadelis et al. 2007. Monitoring Sleepiness with On-board Electrophysiological Recordings for Preventing Sleep-deprived Traffic Accidents. Clinical Neurophysiology. 118(9): 1906-1922.

Doi: https://doi.org/10.1016/j.clinph.2007.04.031.

M. Golmohammadi, A. H. Harati Nejad Torbati, S. Lopez De Diego, I. Obeid, and J. Picone. 2019. Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures. Frontiers in Human Neuroscience. 13: 76.

Doi: https://doi.org/10.3389/fnhum.2019.00076.

A. Kosmadopoulos et al. 2017. The Efficacy of Objective and Subjective Predictors of Driving Performance during Sleep Restriction and Circadian Misalignment. Accident Analysis & Prevention. 99: 445-451.

Doi: https://doi.org/10.1016/j.aap.2015.10.014.

M. Poursadeghiyan, A. Mazloumi, G. N. Saraji, A. Niknezhad, A. Akbarzadeh, and M. H. Ebrahimi. 2017. Determination the Levels of Subjective and Observer Rating of Drowsiness and Their Associations with Facial Dynamic Changes. Iranian Journal of Public Health. 46(1): 93-102.

S. Baiardi, C. La Morgia, L. Sciamanna, A. Gerosa, F. Cirignotta, and S. Mondini. 2018. Is the Epworth Sleepiness Scale a Useful Tool for Screening Excessive Daytime Sleepiness in Commercial Drivers? Accident Analysis & Prevention. 110: 187-189.

Doi: https://doi.org/10.1016/j.aap.2017.10.008.

K. Takahashi, J.-S. Lin, and K. Sakai. 2008. Neuronal Activity of Orexin and Non-orexin Waking-active Neurons during Wake–sleep States in the Mouse. Neuroscience. 153(3): 860-870.

Doi: https://doi.org/10.1016/j.neuroscience.2008.02.058.

T. Sakurai. 2007. The Neural Circuit of Orexin (hypocretin): Maintaining Sleep and Wakefulness. Nature Reviews Neuroscience. 8(3): 171-181.

Doi: https://doi.org/10.1038/nrn2092.

N. Pham et al. 2023. Detection of Microsleep Events with a Behind-the-Ear Wearable System. IEEE Transactions on Mobile Computing. 22(2): 841-857.

Doi: https://doi.org/10.1109/TMC.2021.3090829.

M. T. R. Peiris, R. D. Jones, P. R. Davidson, G. J. Carroll, and P. J. Bones. 2006. Frequent Lapses of Responsiveness during an Extended Visuomotor Tracking Task in Non‐sleep‐deprived Subjects. Journal of Sleep Research 15(3): 291-300.

Doi: https://doi.org/10.1111/j.1365-2869.2006.00545.x.

A. Paul, L. N. Boyle, J. Tippin, and M. Rizzo. 2005. Variability of Driving Performance During Microsleeps. Driving assessment 2005 : Proceedings of the 3rd International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, Rockport, Maine, USA: University of Iowa, 2005. 18-24.

Doi: https://doi.org/10.17077/drivingassessment.1138.

S. S. D. P. Ayyagari, R. D. Jones, and S. J. Weddell. 2021. Detection of Microsleep States from the EEG: A Comparison of Feature Reduction Methods. Medical & Biological Engineering & Computing. 59(7-8): 1643-1657.

Doi: https://doi.org/10.1007/s11517-021-02386-y.

T. Alotaiby, F. E. A. El-Samie, S. A. Alshebeili, and I. Ahmad. 2015. A Review of Channel Selection Algorithms for EEG Signal Processing. EURASIP Journal on Advances in Signal Processing. 2015(1): 66.

Doi: https://doi.org/10.1186/s13634-015-0251-9.

Y. Yang, O. Kyrgyzov, J. Wiart, and I. Bloch. 2013. Subject-specific Channel Selection for Classification of Motor Imagery Electroencephalographic Data. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada: IEEE. 1277-1280.

Doi: https://doi.org/10.1109/ICASSP.2013.6637856.

J. Jin, Y. Miao, I. Daly, C. Zuo, D. Hu, and A. Cichocki. 2019. Correlation-based Channel Selection and Regularized Feature Optimization for MI-based BCI. Neural Networks. 118: 262-270.

Doi: https://doi.org/10.1016/j.neunet.2019.07.008.

H. Varsehi and S. M. P. Firoozabadi. 2021. An EEG Channel Selection Method for Motor Imagery based Brain–computer Interface and Neurofeedback using Granger Causality. Neural Networks.133: 193-206.

Doi: https://doi.org/10.1016/j.neunet.2020.11.002.

J. Jin et al. 2018. Bispectrum-based Channel Selection for Motor Imagery Based Brain-computer Interfacing. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 28(10): 2153-2163.

Doi: https://doi.org/10.1109/TNSRE.2020.3020975.

Y.-H. Liu, S. Huang, and Y.-D. Huang. 2017. Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher’s Criterion-based Channel Selection. Sensors. 17(7): 1557.

Doi: https://doi.org/10.3390/s17071557.

N. Pham et al. 2020. WAKE: A Behind-the-ear Wearable System for Microsleep Detection. Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services, Toronto Ontario Canada: ACM. 404-418.

Doi: https://doi.org/10.1145/3386901.3389032.

M. Golz, D. Sommer, M. Chen, U. Trutschel, and D. Mandic. 2007. Feature Fusion for the Detection of Microsleep Events. The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology. 49(2): 329-342.

Doi: https://doi.org/10.1007/s11265-007-0083-4.

S. J. Weddell, S. Ayyagari, and R. D. Jones. 2021. Reservoir Computing Approaches to Microsleep Detection. Journal of Neural Engineering. 18(4): 046021.

Doi: https://doi.org/10.1088/1741-2552/abcb7f.

Y.-S. Kweon, H.-G. Kwak, G.-H. Shin, and M. Lee. 2021. Automatic Micro-sleep Detection under Car-driving Simulation Environment using Night-sleep EEG. 2021 9th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South): IEEE. 1-6.

Doi: https://doi.org/10.1109/BCI51272.2021.9385325.

R. Jabbar, M. Shinoy, M. Kharbeche, K. Al-Khalifa, M. Krichen, and K. Barkaoui. 2020. Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application. 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar: IEEE. 237-242.

Doi: https://doi.org/10.1109/ICIoT48696.2020.9089484.

D. Martinez-Maradiaga and G. Meixner. 2017. Morpheus Alert: A Smartphone Application for Preventing Microsleeping with a Brain-computer-interface. 2017 4th International Conference on Systems and Informatics (ICSAI), Hangzhou: IEEE. 137-142.

Doi: https://doi.org/10.1109/ICSAI.2017.8248278.

R. T. Puteri and F. Utaminingrum. 2020. Micro-sleep Detection using Combination of haar Cascade and Convolutional Neural Network. Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology, Malang Indonesia: ACM. 130-135.

Doi: https://doi.org/10.1145/3427423.3427433.

A. Malafeev, A. Hertig-Godeschalk, D. R. Schreier, J. Skorucak, J. Mathis, and P. Achermann. 2021. Automatic Detection of Microsleep Episodes with Deep Learning. Frontiers in Neuroscience. 5: 564098.

Doi: https://doi.org/10.3389/fnins.2021.564098.

J. Hu and P. Wang. 2017. Noise Robustness Analysis of Performance for EEG-based Driver Fatigue Detection using Different Entropy Feature Sets. Entropy. 19(8): 385.

Doi: https://doi.org/10.3390/e19080385.

T. Tuncer, S. Dogan, F. Ertam, and A. Subasi. 2021. A Dynamic Center and Multi Threshold Point based Stable Feature Extraction Network for Driver Fatigue Detection Utilizing EEG Signals. Cognitive Neurodynamics. 15(2): 223-237.

Doi: https://doi.org/10.1007/s11571-020-09601-w.

J. J. Bird, L. J. Manso, E. P. Ribeiro, A. Ekart, and D. R. Faria. 2018. A Study on Mental State Classification using EEG-based Brain-Machine Interface. 2018 International Conference on Intelligent Systems (IS), Funchal - Madeira, Portugal: IEEE. 795-800.

Doi: https://doi.org/10.1109/IS.2018.8710576.

A. Norsepri, N. Sulaiman, M. Mustafa, M. M. Hasan, and S. A. Mohd Aris. 2023. A Study of Engineering Student IQ and EQ Based on Analyzing of Electroencephalogram (EEG) Signals. MEKATRONIKA. 5(2): 57-66.

Doi: https://doi.org/10.15282/mekatronika.v5i2.9853.

A. N. Belkacem and A. Lakas. 2021. A Cooperative EEG-based BCI Control System for Robot–Drone Interaction. 2021 International Wireless Communications and Mobile Computing (IWCMC), Harbin City, China: IEEE. 297-302.

Doi: https://doi.org/10.1109/IWCMC51323.2021.9498781.

Haiping Lu, How-Lung Eng, Cuntai Guan, K. N. Plataniotis, and A. N. Venetsanopoulos. 2010. Regularized Common Spatial Pattern with Aggregation for EEG Classification in Small-Sample Setting. IEEE Transactions on Biomedical Engineering. 57(12): 2936-2946.

Doi: https://doi.org/10.1109/TBME.2010.2082540.

C. M. Thibeault and N. Srinivasa. 2013. Using a Hybrid Neuron in Physiologically Inspired Models of the basal Ganglia. Frontiers in Computational Neuroscience. 7.

Doi: https://doi.org/10.3389/fncom.2013.00088.

C.-S. Huang, C.-L. Lin, L.-W. Ko, S.-Y. Liu, T.-P. Su, and C.-T. Lin. 2014. Knowledge-based Identification of Sleep Stages based on Two Forehead Electroencephalogram Channels. Frontiers in Neuroscience. 8.

Doi: https://doi.org/10.3389/fnins.2014.00263.

A. E. Putra, C. Atmaji, and F. Ghaleb. 2018. EEG-based Emotion Classification Using Wavelet Decomposition and K-Nearest Neighbor. 2018 4th International Conference on Science and Technology (ICST), Yogyakarta: IEEE. 1-4.

Doi: https://doi.org/10.1109/ICSTC.2018.8528652.

S. Shukla and R. K. Chaurasiya. 2019. Emotion Analysis Through EEG and Peripheral Physiological Signals Using KNN Classifier. Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB).

Available: https://link.springer.com/book/10.1007/978-3-030-00665-5.

J. Hu. 2017. Automated Detection of Driver Fatigue based on AdaBoost Classifier with EEG Signals. Frontiers in Computational Neuroscience. 11: 72.

Doi: https://doi.org/10.3389/fncom.2017.00072.