meta-learning-in-context-decoding-v3

# Meta-Learning In-Context Brain Decoding ## Overview Enables training-free cross-subject brain decoding through meta-learned in-context learning. The model learns to adapt to new subjects at inference time by conditioning on a small number of reference examples, eliminating the need for per-subject fine-tuning. ## Core Innovation Traditional brain decoders require subject-specific training data. This approach: 1. Meta-learns a general decoding prior across many subjects 2. At inference, adapts to new subjects via in-context examples 3. Achieves competitive accuracy with zero subject-specific training ## Architecture ``` Meta-Training: [Subject A examples] + [Subject B examples] + ... → Learn decoding prior Inference: [New subject context examples] + [Target brain signal] → Decoded output ``` ## In-Context Adaptation ```python class MetaInContextDecoder: def __init__(self, base_model): self.base = base_model def decode(self, brain_signal, context_signals, context_labels): # Concatenate context as "prompt" input_seq = torch.cat([context_signals, brain_signal], dim=1) # Model attends to context to adapt output = self.base(input_seq) # Extract prediction for target position return output[:, -1, :] ``` ## Key Advantages - **Zero-shot generalization**: No training data needed for new subjects - **Few-shot improvement**: Adding more context examples improves accuracy - **Scalable**: Works across different recording modalities - **Practical**: Eliminates per-subject calibration sessions ## Applications - Rapid BCI deployment (no calibration) - Clinical neuroimaging across diverse populations - Multi-subject neuroscience studies - Visual reconstruction from brain activity ## Related Skills - eeg-ieeg-bridge, eeg-foundation-models, brain-foundation-model-batch-effects