---
language: en
tags:
- eeg
- meg
- pytorch
- neuroimaging
license: mit
datasets:
- gabrycina/eeg2meg-tiny
metrics:
- mse
---

# EEG to MEG Prediction Model

This model was trained to predict MEG signals from EEG recordings.

## Training Configuration
- Dataset: gabrycina/eeg2meg-tiny
- Batch Size: 32
- Learning Rate: 0.0001
- Device: mps
- Training Date: 20250104_185119

## Performance
- Best Validation Loss: 0.171059
- Best Epoch: 100

## Model Description

This model uses a deep learning architecture to predict MEG signals from EEG recordings. The architecture includes:
- Frequency and temporal convolutions for feature extraction
- Multi-head attention mechanisms for sensor relationships
- Residual connections for better gradient flow
- Separate prediction heads for magnetometers and gradiometers

## Usage

```python
import torch

# Load the model
model = torch.load('best_model.pth')

# Prepare your EEG data (shape: [batch_size, channels, time_points])
# Make predictions
with torch.no_grad():
    meg_predictions = model(eeg_data)
```