Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts
arXiv:2604.16926v2 Announce Type: replace-cross Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by distribution shifts across clinical settings, devices, and populations. Test-time adaptation (TTA) offers a promising solution by enabling models to adapt to unlabeled target data during inference without access to source data, a valuable property in healthcare settings constrained by privacy regulations and limited labeled data. However, its effe
The proliferation of AI foundation models in sensitive domains like healthcare is driving urgent research into practical deployment challenges such as data privacy and real-world operational variability.
This development addresses a critical hurdle for deploying powerful AI in healthcare by enabling models to adapt to new data environments without compromising privacy, thus expanding their utility and accessibility.
The ability of EEG foundation models to adapt at test-time under distribution shifts means patient data can be processed on-device without needing to be shared, improving privacy and enabling wider clinical application.
- · Healthcare AI providers
- · Patients with neurological conditions
- · Institutions with strict data privacy requirements
- · Edge AI hardware developers
- · Traditional EEG diagnostic methods
- · Centralized cloud-based AI solutions
Increased adoption of AI in clinical neuroscience due to enhanced privacy and adaptability.
Development of new AI-powered diagnostic and monitoring tools for various neurological disorders.
Potential for an accelerate shift towards federated learning or on-device AI across healthcare, setting a precedent for other privacy-sensitive industries.
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Read at arXiv cs.AI