Selection of Neural Oscillatory Features for Human Stress Classification with Single Channel EEG Headset

Sanay Muhammad Umar Saeed · Syed Muhammad Anwar · Muhammad Majid · Muhammad Awais · Majdi R. Alnowami

65 citations0/0 files storedBioMed Research International

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Abstract

A study on classification of psychological stress in humans using electroencephalography (EEG) is presented. The stress is classified using a correlation-based feature subset selection method that efficiently reduces the feature vector length. In this study, twenty-eight participants are involved by filling in the perceived stress scale-10 (PSS-10) questionnaire and their EEG is also recorded in closed eye condition to measure the baseline stress. The recorded data is labelled on the basis of the stress level that is indicated by the participant's PSS score. The feature selection method has shown that, among the EEG oscillations, low beta, high beta, and low gamma are the most significant neural oscillations for classifying human stress. The proposed method not only reduces the time to build a classification model but also improves the classification accuracy up to 78.57% using a single channel wearable EEG device.

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References

1983A Global Measure of Perceived Stress 2012Objective measures, sensors and computational techniques for stress recognition and classification: A survey 2016Human emotion recognition and analysis in response to audio music using brain signals 2016Stress Assessment by Prefrontal Relative Gamma

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