**5. Conclusions**

In this paper, two different labeling methods were used for the classification of long-term stress in humans using EEG signals. Forty-five signal features were analyzed for the classification of chronic stress, and alpha asymmetry was found to be a discriminating feature when using expert's evaluation as ground truth. The PSS scores, when used solely for labeling, returned no significant features. Furthermore, it is evident from our experimental results that SVM and LR give the highest accuracy (85.20%) for classification. We also observed that the stress group was better classified when compared to the control group irrespective of the classifiers used. Finally, we established that alpha asymmetry can be used a potential bio-marker for the classification of long-term stress with SVM. To the best of our knowledge, no previous EEG-based studies have involved a psychology expert for labeling of groups for long-term stress assessment. In the future, more features and participants will be considered for the analysis. With the availability of more data, deep learning based strategies can be applied for potentially improved methods.

**Author Contributions:** Conceptualization, S.M.U.S.; data curation, S.M.U.S. and H.K.; formal analysis, H.K., M.M., and U.B.; methodology, S.M.U.S. and S.M.A.; writing—original draft, S.M.U.S. and S.M.A.; writing—review and editing, S.M.A., M.M., and U.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
