@inproceedings{10.1145/3636534.3698121,
author = {Lloyd, Catherine and Lemoine, Loic Lorente and Al-Shaikh, Reiyan and Ly, Kim Tien and Kayan, Hakan and Perera, Charith and Pham, Nhat},
title = {Stress-GPT: Stress detection with an EEG-based foundation model},
year = {2024},
isbn = {9798400704895},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3636534.3698121},
doi = {10.1145/3636534.3698121},
abstract = {Stress has emerged and continues to be a regular obstacle in people's lives. When left ignored and untreated, it can lead to many health complications, including an increased risk of death. In this study, we propose a foundation model approach for stress detection without the need to train the model from scratch. Specifically, we utilise the foundation model "Neuro-GPT", which was trained on a large open dataset (TUH EEG) with 20,000 EEG recordings. We fine-tune the model for stress detection and evaluate it on a 40-subject open stress dataset. The evaluation results with a fine-tuned Neuro-GPT are promising with an average accuracy of 74.4\% in quantifying "low-stress" and "high-stress". We also conducted experiments to compare the foundation model approach with traditional machine learning methods and highlight several observations for future research in this direction.},
booktitle = {Proceedings of the 30th Annual International Conference on Mobile Computing and Networking},
pages = {2341–2346},
numpages = {6},
keywords = {biosignals, foundation model, cyber-physical systems},
location = {Washington D.C., DC, USA},
series = {ACM MobiCom '24}
}