A Novel Human Stress Level Detection Technique Using EEG
Published in IEEE NMITCON 2023, 2023
In the 21st century, a significant portion of the world’s population is plagued by stress. Stress is harmful to humans and can cause various physical and mental illnesses, such as headaches, anxiety, depression, and heart diseases. This study designs a machine learning-based model to measure the mental stress level of a person using electroencephalography (EEG) signals of the frontal lobe.
The proposed stress level assessment technique classifies the stress level of a person into four categories: no stress, low stress, moderate stress, and high stress. EEG signals of the subjects were recorded while they solved four mathematical question sets with different complexity levels. Eight handcrafted features — Skewness, Kurtosis, Maximum, Mean, Mean Absolute Value, Minimum, Standard Deviation, and Power Spectral Density — were extracted from the pre-processed EEG signals. A majority voting-based ensemble classifier combining Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Naive Bayes predicts a person’s mental stress level, achieving a classification accuracy of 93.85%.
Recommended citation: Konar, D., De, S., Mukherjee, P., & Halder Roy, A. (2023). "A Novel Human Stress Level Detection Technique Using EEG." IEEE NMITCON.
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