Analysis of Stress Using Electroencephalogram Data for Feature Extraction
Keywords:
Human Stress, EEG signal, Feature extraction, BCIAbstract
Patient denial, insensitivity, subjective biases, and inaccuracy are only some of the issues that arise from relying solely on doctor-patient interaction and scale analysis when diagnosing Stress. The creation of an objective, computerized approach for predicting therapeutic outcomes is crucial for enhancing the precision of Stress diagnosis and treatment. In an effort to better detect Stress, this research modifies EEG data and use machine learning algorithms. Ten participants' EEGs were recorded using a Narosky system while they were exposed to various emotional face cues. Psychologists relied on the EEG signal as a diagnostic tool for Stress. Machine learning and deep learning were the methods that handled the feature processing. Using PCA, ICA, and EMD for BCI applications yields significant results. Using SVM, a programmer can reap many benefits: The stress and pressure can be detected by employing EEG signals, and PCA has great generalization properties. The effect of overtraining is particularly vulnerable to the curse-of-dimensionality when the signals are negative. The use of EEG signals for stress detection allowed for these benefits to be realized. The experimental study provides a somewhat comprehensive summary of the various methods, all of which rely on frequency domain analysis of 14 EEG data.
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Marcel Trotzek , Sven Koitka , and Christoph M. Friedrich, “Utilizing Neural Networks and Linguistic Metadata for Early Detection of Stress Indications in Text Sequences”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 32, NO. 3, MARCH 2020.
Systems, C., “ EEG-Based stress detection system using human emotions”, (2018), 10,2360–2370.
Thi, T., Pham, D., Kim, S., Lu, Y., Jung, S., Won, C., “Facial Action Units-Based Image Retrieval for Facial Expression Recognition”, IEEE Access, 7, 5200–5207.https://doi.org/10.1109/ACCESS.2018.2889852 , (2019).
N.d. ,“shocking statistics of workplace stress you never knew - harish saras.” accessed february 1, 2019. Https://www.harishsaras.com/stress-management/shocking-statistics-of-workplace-stress/.
Viegas, carla, and roymaxion , “towards independent stress detection: a dependent model using facial action units.” 2018 international conference on content-based multimedia indexing (cbmi), 1–6.
Woo, seong-woo, “classification of stress and non-stress condition using functional near-infrared spectroscopy.” 2018 18th international conference on control, automation and systems (iccas), no. Iccas: 1147–51.
Wan-Young Chung, Teak-Wei Chong, and Boon-Giin Lee ,” METHODS TO DETECT AND REDUCE DRIVER STRESS: A REVIEW,” International Journal of Automotive Technology, Vol. 20, No. 5, pp. 1051-1063 (2019) DOI 10.1007/s12239-019-0099-3.
“Shocking Statistics of Workplace Stress You Never Knew - Harish Saras.” n.d. Accessed February 1, 2019. https://www.harishsaras.com/stress-management/shocking-statistics-of-workplace-stress/.
M. Tarun Kumar, R. Sandeep Kumar, K. Praveen Kumar, S. Prasanna, G. Shiva,” Health Monitoring and Stress Detection System,” an International Research Journal of Engineering and Technology (IRJET) Volume: 06 Issue: 03 | Mar 2019.
Luis G. Hernández , Oscar Martinez Mozos, José M. Ferrández and Javier M. Antelis,” EEG-Based Detection of Braking Intention Under Different Car Driving Conditions,” Frontiers in Neuroinformatics, vol. 12, May 2018.
Khalid masood and mohammed a. Alghamdi,” modeling mental Stress Using a Deep Learning Framework,” IEEE Access Vol.7 , 2019.
Patil, A. ., & Govindaraj, S. K. . (2023). ADL-BSDF: A Deep Learning Framework for Brain Stroke Detection from MRI Scans towards an Automated Clinical Decision Support System. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 11–23. https://doi.org/10.17762/ijritcc.v11i3.6195
Dhabliya, D. (2021). Feature Selection Intrusion Detection System for The Attack Classification with Data Summarization. Machine Learning Applications in Engineering Education and Management, 1(1), 20–25. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/8
Juneja, V., Singh, S., Jain, V., Pandey, K. K., Dhabliya, D., Gupta, A., & Pandey, D. (2023). Optimization-based data science for an IoT service applicable in smart cities. Handbook of research on data-driven mathematical modeling in smart cities (pp. 300-321) doi:10.4018/978-1-6684-6408-3.ch016 Retrieved from www.scopus.com
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