From Theory to Application: Fine-Tuning Large EEG Model with Real-World Stress Data

TL;DR

This paper demonstrates that fine-tuning a large EEG model on real-world stress data significantly improves stress classification accuracy and efficiency, showcasing the potential of foundation models in practical brain-computer interface applications.

Contribution

It introduces the first application of fine-tuning a large EEG foundation model on real-world stress data, highlighting its effectiveness over traditional classifiers.

Findings

Achieved 90.47% balanced accuracy in stress classification.

Fine-tuned LEM outperforms traditional classifiers in accuracy and inference speed.

Model robustness confirmed under data shuffling and reduced channels.

Abstract

Recent advancements in Large Language Models have inspired the development of foundation models across various domains. In this study, we evaluate the efficacy of Large EEG Models (LEMs) by fine-tuning LaBraM, a state-of-the-art foundation EEG model, on a real-world stress classification dataset collected in a graduate classroom. Unlike previous studies that primarily evaluate LEMs using data from controlled clinical settings, our work assesses their applicability to real-world environments. We train a binary classifier that distinguishes between normal and elevated stress states using resting-state EEG data recorded from 18 graduate students during a class session. The best-performing fine-tuned model achieves a balanced accuracy of 90.47% with a 5-second window, significantly outperforming traditional stress classifiers in both accuracy and inference efficiency. We further evaluate the robustness of the fine-tuned LEM under random data shuffling and reduced channel counts. These results demonstrate the capability of LEMs to effectively process real-world EEG data and highlight their potential to revolutionize brain-computer interface applications by shifting the focus from model-centric to data-centric design.