Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts

arXiv:2604.16926v1 Announce Type: 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 health