Machine Learning-Based Stress Level Detection from EEG Signals
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/fam...
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Main Authors: | , , , , |
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Format: | Conference or Workshop Item |
Language: | English English |
Published: |
IEEE
2021
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Subjects: | |
Online Access: | http://irep.iium.edu.my/92211/1/92211_Machine%20Learning-Based%20Stress%20Level%20Detection%20from%20EEG%20Signals.pdf http://irep.iium.edu.my/92211/7/92211_Machine%20Learning-Based%20Stress%20Level%20Detection%20from%20EEG%20Signals_Scopus.pdf http://irep.iium.edu.my/92211/ https://ieeexplore.ieee.org/abstract/document/9526333 |
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Summary: | 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%. |
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