meta-learning-in-context-decoding-v3
Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding. Uses meta-learned in-context learning to decode brain signals across subjects without any subject-specific training. Supports visual decoding from fMRI/EEG signals with zero-shot generalization. Activation: meta-learning brain decoding, in-context learning, cross-subject, training-free decoding, zero-shot brain decoding, visual reconstruction, 元学习脑解码, 跨被试解码, 零样本解码
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- Repository
- hiyenwong/ai_collection
- Last source activity
- June 4, 2026 at 13:32
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- English
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- meta-learning-in-context-decoding-v3
- description
- Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding. Uses meta-learned in-context learning to decode brain signals across subjects without any subject-specific training. Supports visual decoding from fMRI/EEG signals with zero-shot generalization. Activation: meta-learning brain decoding, in-context learning, cross-subject, training-free decoding, zero-shot brain decoding, visual reconstruction, 元学习脑解码, 跨被试解码, 零样本解码
- version
- 1.0.0
- metadata
- {"hermes":{"source_paper":"Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding","arxiv_id":"2604.08537","tags":["meta-learning","brain-decoding","cross-subject","in-context","zero-shot"]}}
# 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
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