Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts
The episode discusses a study on EEG foundation models and Test-Time Adaptation (TTA) under real-world distribution shifts. The hosts examine how TTA often fails or degrades performance when encountering data variations, such as those seen in clinical datasets. The conclusion is that reliability and robustness are more critical than raw accuracy for deploying trustworthy AI systems.