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Unsupervised EEG domain adaptation is unlocked by treating distribution shifts as linear transformations on the SPD manifold, enabling interpretable alignment without labeled calibration data.
Unlabeled physiological signals unlock multimodal EEG foundation model adaptation, matching supervised performance with zero-shot generation.
Autonomous LLM agents guided by early learning-curve forecasting can discover generalist EEG architectures that outperform state-of-the-art foundation models across 14 diverse clinical and affective benchmarks.
