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

This study demonstrates that fine-tuned Large EEG Models, specifically LaBraM, can accurately classify stress levels in real-world classroom settings, achieving a balanced accuracy of over 90%. The findings highlight the potential of LEMs to enhance brain-computer interfaces by effectively processing real-world EEG data beyond controlled clinical environments.