EEG Based Classification of Long-Term Stress Using Psychological Labeling

Sanay Muhammad Umar Saeed · Syed Muhammad Anwar · Humaira Khalid · Muhammad Majid · Ulaş Bağcı

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Heart Rate Variability and Autonomic Control

Abstract

Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

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References

1990Chronic stress, acute stress, and depressive symptoms 2002Neurobiology of early life stress: Clinical studies 2017Machine Learning Framework for the Detection of Mental Stress at Multiple Levels 2019Human stress classification using EEG signals in response to music tracks 2019Classification of Perceived Mental Stress Using A Commercially Available EEG Headband 2019A Novel Wearable EEG and ECG Recording System for Stress Assessment 2017Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach 2016EEG based stress level identification

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