Lloyd, Catherine ![]() ![]() |
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) |
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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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