From Theory to Application: Fine-Tuning Large EEG Model with Real-World Stress Data
Journal:
arXiv
Published Date:
May 29, 2025
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.
