Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts
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 effectiveness for EEG remains largely underexplored. In this work, we introduce NeuroAdapt-Bench, a systematic benchmark for evaluating test-time adaptation me...
| Authors | Gabriel Jason Lee, Jathurshan Pradeepkumar, Jimeng Sun |
|---|---|
| Primary category | cs.LG |
| Published | 18 April 2026 |
| Last updated at source | 25 August 2026 |
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