metadata
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
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)