arXiv:2605.13930v3 Announce Type: replace Abstract: EEG foundation models achieve state-of-the-art clinical performance, yet the internal computations driving their predictions remain opaque: a barrier to clinical trust. We apply TopK Sparse Autoencoders (SAEs) across three architecturally distinct EEG transformers: SleepFM, REVE, and LaBraM to extract sparse feature dictionaries from their embeddings. By grounding these features in a clinical taxonomy (abnormality, age, sex, and medication), we benchmark monosemanticity and entanglement across architectures. A single hyperparameter procedure,
The increasing sophistication of AI foundation models in sensitive domains like healthcare necessitates interpretability to build trust and ensure responsible deployment.
Improving the trustworthiness and explainability of sophisticated AI models is critical for their adoption in regulated and high-stakes fields such as clinical medicine, impacting both ethical development and widespread use.
The ability to mechanistically interpret EEG foundation models will allow for better debugging, bias detection, and clinical validation, potentially accelerating their integration into medical practice.
- · AI ethicists
- · Healthcare AI developers
- · Patients
- · Neuroscience researchers
- · Opaque AI systems
- · Developers neglecting interpretability
This research provides a framework for understanding complex AI models in electroencephalography.
Increased transparency and trust will accelerate the clinical adoption of AI-powered diagnostic tools for neurological conditions.
The development of 'interpretable AI' will become a standard requirement for all sensitive applications, shifting the paradigm of AI development towards explainability by design.
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Read at arXiv cs.LG