arXiv:2605.26910v1 Announce Type: new Abstract: Large EEG Foundation Models (FMs) have shown great potential for decoding EEG signals across diverse cognitive tasks. However, existing EEG-FM studies exhibit three critical limitations: opaque supervised baseline tuning, unverified contributions of complex learning paradigms, and a lack of transparency in model decision-making. To address these, we propose EEG-FM-Audit, a comprehensive evaluation and analysis pipeline designed to systematize the assessment of EEG-FMs. EEG-FM-Audit consists of three primary components: (1) an ASHA-driven benchmar
The proliferation of EEG Foundation Models (FMs) necessitates standardized evaluation as their complexity and impact grow, addressing previous limitations in their assessment.
A systematic audit pipeline for EEG Foundation Models improves transparency, reliability, and accelerate the responsible development and deployment of advanced brain-computer interface technologies.
The rigor and comparability of research and development in EEG Foundation Models will increase, leading to more robust and trustworthy applications.
- · AI researchers
- · neuroscience community
- · medical technology companies
- · patients benefiting from advanced EEG applications
- · developers of unverified or opaque EEG models
- · research methods lacking systematic validation
Improved trust and accelerated development of EEG Foundation Models.
Faster integration of advanced EEG capabilities into clinical and commercial applications like diagnostics and neuroprosthetics.
Enhanced understanding of brain function and more intuitive, reliable brain-computer interfaces (BCIs) becoming commonplace.
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Read at arXiv cs.LG