brainfuse-unified-infra

---
name: brainfuse-unified-infra
description: "BrainFuse: Unified infrastructure for multimodal neuroimaging data processing (EEG, MEG, fMRI). Implements automated preprocessing pipelines, modality-specific feature extraction, cross-modal registration, and joint analysis workflows. Activation: brainfuse, neuroimaging pipeline, multimodal brain data, eeg fmri meg processing, brain data fusion, neuroimaging infrastructure, cross-modal registration"
---

# BrainFuse: Unified Neuroimaging Infrastructure

## Overview
A unified infrastructure framework for processing and analyzing multimodal neuroimaging data. Addresses the challenge of integrating heterogeneous brain imaging modalities (EEG, MEG, fMRI) into a coherent analysis pipeline.

## Key Contributions

1. **Unified Data Model**: Common representation across EEG, MEG, and fMRI
2. **Automated Preprocessing**: Modality-specific pipelines with standardized outputs
3. **Cross-Modal Registration**: Temporal and spatial alignment between modalities
4. **Joint Analysis Framework**: Fused feature extraction and multimodal decoding

## Core Architecture

```
BrainFuse Pipeline:
  Raw Data → Modality-Specific Preprocessing → Feature Extraction
    → Cross-Modal Registration → Joint Analysis → Results
```

### Modality-Specific Processing

| Modality | Preprocessing | Features |
|----------|--------------|----------|
| EEG | Filtering, ICA, artifact removal | Spectral power, ERPs, connectivity |
| MEG | Maxwell filtering, source localization | Oscillatory power, connectivity |
| fMRI | Motion correction, normalization | BOLD time series, functional connectivity |

### Cross-Modal Registration

- **Temporal alignment**: Resample to common time grid
- **Spatial registration**: Map EEG/MEG source space to fMRI voxels
- **Feature fusion**: Concatenate, weighted averaging, or learned fusion

## Implementation

```python
class BrainFusePipeline:
    def __init__(self, modalities=['eeg', 'fmri']):
        self.modalities = modalities
        self.preprocessors = {m: self._get_preprocessor(m) for m in modalities}
        
    def preprocess(self, raw_data):
        """Run modality-specific preprocessing."""
        processed = {}
        for mod, data in raw_data.items():
            processed[mod] = self.preprocessors[mod].run(data)
        return processed
    
    def register(self, processed_data):
        """Cross-modal spatial-temporal registration."""
        # Register EEG/MEG source estimates to fMRI space
        registered = self._spatial_registration(processed_data)
        registered = self._temporal_alignment(registered)
        return registered
    
    def joint_analysis(self, registered_data):
        """F multimodal analysis."""
        features = self._extract_features(registered_data)
        fused = self._fuse_features(features, method='weighted')
        return fused
```

## Practical Applications
- **Brain-Computer Interfaces**: Multimodal feature fusion for improved decoding
- **Clinical diagnosis**: Combined biomarkers from multiple modalities
- **Cognitive neuroscience**: Cross-modal validation of findings
- **Large-scale studies**: Standardized processing across sites

## Limitations
- Requires careful calibration across modalities
- Source localization accuracy affects EEG/fMRI alignment
- Computational cost scales with data volume

## Paper Reference
- **Title**: BrainFuse: Unified Infrastructure for Multimodal Neuroimaging
- **arXiv**: [2604.13847v1](https://arxiv.org/abs/2604.13847v1)
- **Date**: April 18, 2026
- **Categories**: q-bio.QM, eess.IV