Stress-GPT: Stress detection with an EEG-based foundation model

Lloyd, Catherine ORCID: https://orcid.org/0000-0002-7056-8158, Lemoine, Loic Lorente, Al-Shaikh, Reiyan, Ly, Kim Tien, Kayan, Hakan, Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 and Pham, Nhat 2024. Stress-GPT: Stress detection with an EEG-based foundation model. Presented at: ACM MobiCom '24: 30th Annual International Conference on Mobile Computing and Networking, Washington DC, USA, 18-22 November 2024. Proceedings of the 30th Annual International Conference on Mobile Computing and Networking. New York: ACM, pp. 2341-2346. 10.1145/3636534.3698121

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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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering Computer Science & Informatics
Publisher:	ACM
ISBN:	979-8-4007-0489-5
Last Modified:	20 Dec 2024 16:07
URI:	https://orca.cardiff.ac.uk/id/eprint/174888

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