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This paper introduces ZeroMAG, a zero-shot framework for adapting frozen EEG foundation models to multimodal recordings using unlabeled target data. By constructing modality-subject-task conditions and generating adapter weights in a function-constrained latent space, ZeroMAG effectively integrates complementary physiological signals without requiring target labels or optimization. The approach achieves significant performance gains over EEG-only inference and direct weight regression, approaching supervised multimodal adaptation performance.
Unlabeled physiological signals unlock multimodal EEG foundation model adaptation, matching supervised performance with zero-shot generation.
EEG foundation models (EFMs) capture reusable knowledge from large-scale EEG data, while many EEG recordings also include companion physiological signals that provide complementary information beyond the EEG-only interface. The challenge is to preserve this pretrained knowledge while extending the EFM to heterogeneous multimodal recordings through an adaptation inferred from unlabeled target data. We introduce ZeroMAG, a zero-shot multimodal adapter generation framework that extends a frozen EEG encoder and prediction head using unlabeled target recordings, without target labels or target-side optimization. The target datasets are held out from all model training and selection in the ZeroMAG pipeline. ZeroMAG organizes companion modalities around a configuration-invariant adapter, constructs a modality-subject-task condition from unlabeled recordings and task context, and generates adapter weights in a function-constrained latent space learned from source adapters. Across six held-out target datasets and three EFM backbones, ZeroMAG improves balanced accuracy by 7.22 percentage points over EEG-only inference and 4.89 points over direct weight regression, while coming within 0.50 points of supervised multimodal adaptation on average. Ablations further show that removing functional supervision from either representation learning or conditional generation degrades generated-adapter performance, confirming the contribution of both components.
