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Machine Learning-Based Stress Level Detection from EEG Signals

Nirabi, Ali and Abd Rhman, Faridah and Habaebi, Mohamed Hadi and Sidek, Khairul Azami and Yusoff, Siti Hajar (2021) Machine Learning-Based Stress Level Detection from EEG Signals. In: 2021 IEEE 7th International Conference on Smart Instrumentation, Measurement and Applications, Bandung, Indonesia.

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Recent statistical studies indicate an increase in mental stress in human beings around the world. Due to the recent pandemic and the subsequent lockdowns, people are suffering from different types of stress for being jobless, financially damaged, loss of business, deterioration of personal/family relationships, etc. Stress could be a severe factor for many common disorders if experienced for a long time. Stress is associated with the brain activities of human beings that can be scanned by electroencephalogram (EEG) signals which is very complex and often challenging to understand the signal's pattern. This paper presented a system to detect the stress level from the EEG signals using machine learning algorithms. The proposed method, at first, removed physiological noises from the EEG signal applying a band-pass FIR filter. A discrete wavelet transform (DWT) method was used for features extraction from the filtered EEG signal. The features were classified using a set of classifiers those are knearest neighbors (kNN), support vector machine (SVM), Naïve Bayes, and linear discriminant analysis (LDA). Two levels of stressed EEG data were considered and found the classification accuracy of 86.3%, 91.0%, 81.7%, and 90.0%. The highest classification accuracy, the SVM classifier, outperforms the current state of the art by 15.8%.

Item Type: Conference or Workshop Item (Plenary Papers)
Uncontrolled Keywords: Stress Level Detection; Machine learning, Braincomputer interface; classification; Mental; EEG Signals, SVM, KNN, LDA, Naïve Bayes
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Kulliyyah of Engineering
Depositing User: Dr. Mohamed Hadi Habaebi
Date Deposited: 17 Sep 2021 15:24
Last Modified: 07 Oct 2021 09:47
URI: http://irep.iium.edu.my/id/eprint/92211

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