Electroencephalography based Classification of Long-term Stress using Psychological Labeling

TL;DR

This study demonstrates that EEG-based alpha asymmetry features, especially when labeled by experts, can effectively classify long-term stress with high accuracy, offering a potential biomarker for stress detection.

Contribution

The paper introduces a novel approach combining EEG features and expert labeling to improve long-term stress classification accuracy.

Findings

Support vector machine achieved up to 85.20% accuracy.

Alpha asymmetry is a promising biomarker for stress detection.

Expert evaluation improved classification performance.

Abstract

Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing.The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities.In this study, long-term stress is classified using baseline EEGsignal recordings. The labelling for the stress and control groupsis performed using two methods (i) the perceived stress scalescore and (ii) expert evaluation. The frequency domain featuresare extracted from five-channel EEG recordings in addition tothe frontal and temporal alpha and beta asymmetries. The alphaasymmetry is computed from four channels and used as a feature.Feature selection is also performed using a t-test to identifystatistically significant features for both stress and control groups.We found that support vector machine is best suited to classifylong-term human stress when used with alpha asymmetry asa feature. It is observed that expert evaluation based labellingmethod has improved the classification accuracy up to 85.20%.Based on these results, it is concluded that alpha asymmetry maybe used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.