Are large brainwave foundation models capable yet? Insights from fine-tuning
File(s)
Published version
Author(s)
Type
Conference Paper
Abstract
Foundation Models have demonstrated significant success across various domains in Artificial Intelligence (AI), yet their capabilities for brainwave modeling remain unclear. In this paper, we comprehensively evaluate current Large Brainwave Foundation Models (LBMs) through systematic fine-tuning experiments across multiple Brain-Computer Interface (BCI) benchmark tasks, including memory tasks and sleep stage classification. Our extensive analysis shows that state-of-the-art LBMs achieve only marginal improvements (0.5%) over traditional deep architectures while requiring significantly more parameters (millions vs thousands), raising important questions about their efficiency and applicability in BCI contexts. Moreover, through detailed ablation studies and Low-Rank Adaptation (LoRA), we significantly reduce trainable parameters without performance degradation, while demonstrating that architectural and training inefficiencies limit LBMs’ current capabilities. Our experiments span both full model fine-tuning and parameter-efficient adaptation techniques, providing insights into optimal training strategies for BCI applications. We pioneer the application of LoRA to LBMs, revealing that performance benefits generally emerge when adapting multiple neural network components simultaneously. These findings highlight the critical need for domain-specific development strategies to advance LBMs, suggesting that current architectures may require redesign to fully leverage the potential of foundation models in brainwave analysis.
Date Issued
2025-07-13
Date Acceptance
2025-07-01
Citation
Proceedings of Machine Learning Research, 2025, 267, pp.32878-32888
ISSN
2640-3498
Publisher
MLResearchPress
Start Page
32878
End Page
32888
Journal / Book Title
Proceedings of Machine Learning Research
Volume
267
Copyright Statement
© The authors and PMLR 2025. MLResearchPress.
Source
International Conference on Machine Learning (ICML) 2025
Publication Status
Published
Start Date
2025-07-13
Finish Date
2025-07-19
Coverage Spatial
Vancouver, Canada
Date Publish Online
2025-07-01