*Article* **EEG Based Classification of Long-Term Stress Using Psychological Labeling**

**Sanay Muhammad Umar Saeed 1, Syed Muhammad Anwar 2,3,\*, Humaira Khalid 4, Muhammad Majid 1 and Ulas Bagci 3**


Received: 18 February 2020; Accepted: 25 March 2020; Published: 29 March 2020

**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 managemen<sup>t</sup> 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.

**Keywords:** long-term stress; electroencephalography; machine learning; perceived stress scale; expert evaluation
