gabrycina/eeg2meg-model-tiny

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)