MACHINE LEARNING APPROACH FOR MULTI-CLASS STRESS ASSESSMENT WITH ELECTROENCEPHALOGRAPHY SIGNALS
Main Article Content
Keywords
EEG, PSS, Stress Detection, Machine Learning, Classification, Physiological Data
Abstract
This paper focuses on utilizing Electroencephalography (EEG) signals and machine learning techniques in developing an objective stress assessment framework. The study aimed to investigate the correlation between EEG and Perceived Stress Scale (PSS) by utilizing data segmentation technique. The PSS scores are employed to record perceived stress levels of individuals. These PSS scores serve as the basis for categorizing the data into three classes: i) two class: stressed and non-stressed ii) three class: stressed, mildly stressed, and non-stressed, iii) four class: highly stressed, moderately stressed, mildly stressed and non-stressed. EEG recordings are captured from 40 participants using 4 channels Inter axon Muse headband, equipped with dry electrodes. The EEG data is segmented into units of 10 seconds. The data is processed to extract five feature sets including Power Spectrum, Rational Asymmetry, Differential Asymmetry, Correlation and Power Spectral Density. The success levels are accessed utilizing classifiers (Naive Bayes, Support Vector Machine, Logistic Regression, Simple Logistic Regression, Random Tree, K-Nearest Neighbor, Bagging, Random Forest, Multilayer Perceptron, AdaBoost). The highest accuracies achieved for two-, three-, and four-class stress classification are 91.52%, 88.47%, and 87.36%, respectively. These accuracies are obtained using the Adaboost classifier for two-class classification, the Random Forest classifier for three-class classification, and the Adaboost classifier again for four-class classification. These findings underline the importance of the chosen features and classifiers in increasing the prediction accuracy while contributing to the existing knowledge on stress detection with EEG Signals.
References
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9. A. Zaitcev, G. Cook, W. Liu, M. Paley, and E. Milne, "Feature extraction for BCIs based on electromagnetic source localization and multiclass Filter Bank Common Spatial Patterns," in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015, vol. 2015-November.
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19. S. M. U. Saeed, S. M. Anwar, M. Majid, M. Awais, and M. Alnowami, "Selection of Neural Oscillatory Features for Human Stress Classification with Single Channel EEG Headset," BioMed Research International, vol. 2018, 2018.
20. S. K. Panigrahy, S. K. Jena, and A. K. Turuk, "Study and Analysis of Human Stress Detection using Galvanic Skin Response (GSR) Sensor in Wired and Wireless Environments," Research Journal of Pharmacy and Technology, vol. 10, no. 2, 2017.
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22. A. Arsalan, M. Majid, S. M. Anwar, and U. Bagci, "Classification of Perceived Human Stress using Physiological Signals," in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019.
23. M. Majid, A. Arsalan, and S. M. Anwar, "A Multimodal Perceived Stress Classification Framework using Wearable Physiological Sensors," arXiv preprint arXiv:2206.10846, 2022.
24. H. Jebelli, M. M. Khalili, S. Hwang, and S. H. Lee, "A supervised learning-based construction workers' stress recognition using a wearable electroencephalography (EEG) device," in Construction Research Congress 2018: Safety and Disaster Management - Selected Papers from the Construction Research Congress 2018, 2018, vol. 2018-April.
25. P. Nagar and D. Sethia, "Brain Mapping Based Stress Identification Using Portable EEG Based Device," in 2019 11th International Conference on Communication Systems and Networks, COMSNETS 2019, 2019.
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27. N. Hambali, H. N. N. A. Hassan, Z. H. Murat, and N. I. A. Razak, "The Preliminary Study of Interrelationship of Perceived Stress to Brainwave Characteristic of Breastfeeding Women," in Electrical and Electronic Engineering vol. 5, ed, 2015.
28. S. M. U. Saeed, S. M. Anwar, H. Khalid, M. Majid, and U. Bagci, "EEG based classification of long-term stress using psychological labeling," Sensors (Switzerland), vol. 20, no. 7, 2020.
29. R. Khosrowabadi, "Stress and perception of emotional stimuli: Long-term stress rewiring the brain," Basic and Clinical Neuroscience, vol. 9, no. 2, 2018.
30. B. Fatima, A. Raheel, A. Arsalan, M. Majid, M. Ehatisham-Ul-Haq, and S. M. Anwar, "Gender Recognition using EEG during Mobile Game Play," in 2021 International Conference on Information Technology, ICIT 2021 - Proceedings, 2021.
31. G. Herrera-Arcos et al., "Modulation of neural activity during guided viewing of visual art," Frontiers in Human Neuroscience, vol. 11, 2017.
32. A. Chakraborti et al., "Assessing perceived stress in medical personnel: In search of an appropriate scale for the Bengali population," Indian Journal of Psychological Medicine, vol. 35, no. 1, 2013.
33. M. Guermandi, A. Bigucci, E. F. Scarselli, and R. Guerrieri, "EEG acquisition system based on active electrodes with common-mode interference suppression by Driving Right Leg circuit," in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015, vol. 2015-November.
34. F. Grosselin et al., "Quality assessment of single-channel EEG for wearable devices," Sensors (Switzerland), vol. 19, no. 3, 2019.
35. L. Malviya and S. Mal, "A novel technique for stress detection from EEG signal using hybrid deep learning model," Neural Computing and Applications, vol. 34, no. 22, 2022.
36. P. Garcés et al., "Resting state EEG power spectrum and functional connectivity in autism: a cross-sectional analysis," Molecular autism, vol. 13, no. 1, 2022.
37. Y. Wang, S. Wang, and M. Xu, "Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features," International Journal of Environmental Research and Public Health, vol. 19, no. 2, 2022.
38. R. N. Duan, J. Y. Zhu, and B. L. Lu, "Differential entropy feature for EEG-based emotion classification," in International IEEE/EMBS Conference on Neural Engineering, NER, 2013.
39. M. Giraki et al., "Correlation between stress, stress-coping and current sleep bruxism," Head and Face Medicine, vol. 6, no. 1, 2010.
40. R. Wang, J. Wang, H. Yu, X. Wei, C. Yang, and B. Deng, "Power spectral density and coherence analysis of Alzheimer’s EEG," Cognitive Neurodynamics, vol. 9, no. 3, 2015.
41. J. W. Li et al., "An Approach to Emotion Recognition Using Brain Rhythm Sequencing and Asymmetric Features," Cognitive Computation, vol. 14, no. 6, 2022.
42. Z. Wu et al., "Synthesis, Characterization, Immune Regulation, and Antioxidative Assessment of Yeast-Derived Selenium Nanoparticles in Cyclophosphamide-Induced Rats," ACS Omega, vol. 6, no. 38, 2021.
43. M. Kaestner, M. L. Evans, Y. D. Chen, and A. M. Norcia, "Dynamics of absolute and relative disparity processing in human visual cortex," NeuroImage, vol. 255, 2022.
44. P. Schober and L. A. Schwarte, "Correlation coefficients: Appropriate use and interpretation," Anesthesia and Analgesia, vol. 126, no. 5, 2018.
45. Q. Xiong, X. Zhang, W. F. Wang, and Y. Gu, "A Parallel Algorithm Framework for Feature Extraction of EEG Signals on MPI," Computational and Mathematical Methods in Medicine, vol. 2020, 2020.
46. M. Athar et al., "Iron and Manganese Codoped Cobalt Tungstates Co1–(x+ y) Fe x Mn y WO4 as Efficient Photoelectrocatalysts for Oxygen Evolution Reaction," ACS omega, vol. 6, no. 11, pp. 7334-7341, 2021.
47. Abdullah, I. Faye, and M. R. Islam, "A comparative study on end-to-end deep learning methods for Electroencephalogram channel selection," Engineering Applications of Artificial Intelligence, vol. 122, 2023.
48. A. Gupta, "Feature Selection Techniques in Machine Learning," in
Https://Www.Analyticsvidhya.Com/Blog/2020/10/Feature-Selection-Techniques-in-Machine-Learning/, ed, 2020.
49. F. Al-shargie, T. B. Tang, N. Badruddin, and M. Kiguchi, "Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach," Medical and Biological Engineering and Computing, vol. 56, no. 1, 2018.
50. E. Y. Mohammady, M. R. Soaudy, A. Abdel-Rahman, M. Abdel-Tawwab, and M. S. Hassaan, "Comparative effects of dietary zinc forms on performance, immunity, and oxidative stress-related gene expression in Nile tilapia, Oreochromis niloticus," Aquaculture, vol. 532, 2021.
51. S. L. Cessie and J. C. V. Houwelingen, "Ridge Estimators in Logistic Regression," Applied Statistics, vol. 41, no. 1, 1992.
52. M. Hasan et al., "Synthesis of Loureirin B-Loaded Nanoliposomes for Pharmacokinetics in Rat Plasma," ACS Omega, vol. 4, no. 4, 2019.
53. G. Panchal, A. Ganatra, Y. P. Kosta, and D. Panchal, "Behaviour Analysis of Multilayer Perceptronswith Multiple Hidden Neurons and Hidden Layers," International Journal of Computer Theory and Engineering, 2011.
54. L. Fraiwan, K. Lweesy, N. Khasawneh, H. Wenz, and H. Dickhaus, "Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier," Computer Methods and Programs in Biomedicine, vol. 108, no. 1, 2012.
55. S. Hegelich, "Decision Trees and Random Forests: Machine Learning Techniques to Classify Rare Events," European Policy Analysis, vol. 2, no. 1, 2016.
56. D. R. Edla, K. Mangalorekar, G. Dhavalikar, and S. Dodia, "Classification of EEG data for human mental state analysis using Random Forest Classifier," in Procedia Computer Science, 2018, vol. 132.
57. Y. P. Huang and M. F. Yen, "A new perspective of performance comparison among machine learning algorithms for financial distress prediction," Applied Soft Computing Journal, vol. 83, 2019.
58. R. E. Schapire, "The Boosting Approach to Machine Learning: An Overview," 2003.
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Dec 28, 2023
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How to Cite
Muhammad Usman Mustafa, Saeed Ahmad Buzdar, Ayesha Ikhlaq, Mehrun Nisa, Sadia Malik, Saba Saeed, Muhammad Shahid Khan, & Arshad Javid. (2023). MACHINE LEARNING APPROACH FOR MULTI-CLASS STRESS ASSESSMENT WITH ELECTROENCEPHALOGRAPHY SIGNALS. Journal of Population Therapeutics and Clinical Pharmacology, 30(19), 1464-1478. https://doi.org/10.53555/jptcp.v30i19.3852
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Author Biographies
Muhammad Usman Mustafa
Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
Saeed Ahmad Buzdar
Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
Ayesha Ikhlaq
Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
Mehrun Nisa
Department of Physics, Government Sadiq College Women University, Bahawalpur, 63100, Pakistan
Sadia Malik
Department of Physics, Government Sadiq College Women University, Bahawalpur, 63100, Pakistan
Saba Saeed
Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
Muhammad Shahid Khan
Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
Arshad Javid
Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
How to Cite
Muhammad Usman Mustafa, Saeed Ahmad Buzdar, Ayesha Ikhlaq, Mehrun Nisa, Sadia Malik, Saba Saeed, Muhammad Shahid Khan, & Arshad Javid. (2023). MACHINE LEARNING APPROACH FOR MULTI-CLASS STRESS ASSESSMENT WITH ELECTROENCEPHALOGRAPHY SIGNALS. Journal of Population Therapeutics and Clinical Pharmacology, 30(19), 1464-1478. https://doi.org/10.53555/jptcp.v30i19.3852