eeg2vision-multimodal-eeg-based-framework-2d-visual

---
name: eeg2vision-multimodal-eeg-based-framework-2d-visual
description: "Multimodal EEG-to-image reconstruction framework using diffusion models. Reconstructs 2D visual stimuli from EEG signals. Activation: eeg2vision, eeg image reconstruction, eeg visual decoding, brain-to-image, eeg-to-image"
---

# EEG2Vision: Multimodal EEG-Based Framework for 2D Visual Reconstruction

## Overview

EEG2Vision is a multimodal framework for reconstructing 2D visual stimuli from non-invasive electroencephalography (EEG) signals. It leverages diffusion models and cross-modal alignment to translate brain activity into visual representations.

## Source Paper

- **Title:** EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience
- **arXiv: 2604.08063v1
- **Date:** 2026-04-09
- **Authors:** Emanuele Balloni, Emanuele Frontoni, Chiara Matti et al.
- **PDF: https://arxiv.org/pdf/2604.08063v1

## Core Concepts

### EEG Feature Extraction
- Raw EEG signals to band power features (delta, theta, alpha, beta, gamma)
- Spatial filtering using CSP (Common Spatial Patterns) or Laplacian
- Temporal feature aggregation across fixation windows

### Cross-Modal Alignment
- EEG features to latent space alignment with visual features
- CLIP-based image-text embeddings as bridge modality
- Contrastive learning for EEG-image feature matching

### Diffusion-Based Reconstruction
- Text-to-image diffusion models (Stable Diffusion) as backbone
- EEG-conditioned latent diffusion for visual reconstruction
- Prompt-guided enhancement using decoded semantic categories

## Workflow

EEG Recording to Feature Extraction to Latent Alignment to Diffusion Reconstruction to Image Output

### Step 1: Preprocessing

```python
import mne
import numpy as np

def preprocess_eeg(raw_data, sfreq=250):
    raw_data.filter(1, 45, method='iir')
    epochs = mne.Epochs(raw_data, events, tmin=0, tmax=1.0)
    ica = mne.preprocessing.ICA(n_components=20)
    ica.fit(epochs)
    ica.exclude = [0, 1]
    clean = ica.apply(epochs)
    return clean
```

### Step 2: Feature Extraction

```python
def extract_eeg_features(epochs):
    freq_bands = {'delta': (1, 4), 'theta': (4, 8), 'alpha': (8, 13), 'beta': (13, 30), 'gamma': (30, 45)}
    features = []
    for band, (low, high) in freq_bands.items():
        power = epochs.compute_psd(method='welch', fmin=low, fmax=high).get_data()
        features.append(power.mean(axis=-1))
    return np.concatenate(features, axis=1)
```

### Step 3: Cross-Modal Mapping

```python
import torch

class EEGToCLIPMapper(torch.nn.Module):
    def __init__(self, eeg_dim, clip_dim=512):
        super().__init__()
        self.encoder = torch.nn.Sequential(
            torch.nn.Linear(eeg_dim, 1024),
            torch.nn.ReLU(),
            torch.nn.Linear(1024, clip_dim),
            torch.nn.LayerNorm(clip_dim)
        )
    def forward(self, eeg_features):
        return self.encoder(eeg_features)
```

## Applications

- BCI Communication: Decode visual imagery for locked-in patients
- Dream Recording: Reconstruct visual content during sleep
- Neuroscience Research: Study visual processing pathways
- Cognitive Assessment: Evaluate visual memory and perception

## Limitations

- Spatial resolution limited by EEG (compared to fMRI/ECoG)
- Requires individual subject calibration
- Reconstruction quality depends on training data alignment
- Currently limited to 2D images

## Related Skills

- eeg2vision-multimodal-eeg-framework-v2
- brain-dit-fmri-foundation-model
- meta-learning-in-context-brain-decoding

## Activation Keywords

- eeg2vision, eeg image reconstruction, eeg visual decoding, brain to image, eeg to image, visual reconstruction