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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.
EEG-PRIME achieves zero-shot transfer capabilities in EEG decoding, outperforming traditional models without the need for domain-specific calibration.
Injecting LLM-generated textual descriptions of facial action units into a vision model substantially boosts AU detection performance, suggesting a powerful way to leverage language priors in computer vision.
