Term of Award

Summer 2024

Degree Name

Master of Science, Mechanical Engineering

Document Type and Release Option

Dissertation (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Mechanical Engineering

Committee Chair

Biswanath Samanta

Committee Member 1

Jinki Kim

Committee Member 2

JungHun Choi


A study is presented to investigate self-supervised contrastive learning (SSCL) models using physiological data obtained from non-invasive wearable sensors for mental stress assessment. The present work involved acquisition of electroencephalography (EEG) signals using wearable sensors, signal preprocessing, data augmentation, and investigation of self-supervised contrastive learning (SSCL) algorithms for multi-class mental stress assessment. Seven volunteers participated in this study executing various mental tasks while wearing an OpenBCI head cap to acquire EEG signals. The acquired EEG signals were preprocessed and utilized for data augmentation in time and frequency domains with different SSCL models. Optimal data augmentation combinations and SSCL models were identified leading to multi-class mental stress classification accuracy in the range of 98 to 99.9%. The study demonstrates the effectiveness of SSCL models using relatively small-size unlabeled EEG signals for multi-class mental stress assessment with a reasonably high accuracy. The potential of using SSCL models with EEG signals for mental stress assessment and intervention through human-robot interactions (HRI) is outlined.

Research Data and Supplementary Material


Available for download on Wednesday, June 04, 2025