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README.md +37 -2
best_model.pth +1 -1
config.json +1 -1
metrics.json +201 -201

README.md CHANGED Viewed

@@ -1,3 +1,16 @@
 # EEG to MEG Prediction Model
@@ -8,8 +21,30 @@ This model was trained to predict MEG signals from EEG recordings.
 - Batch Size: 32
 - Learning Rate: 0.0001
 - Device: mps
-- Training Date: 20250104_184834
 ## Performance
-- Best Validation Loss: 0.166563
 - Best Epoch: 100

+---
+language: en
+tags:
+- eeg
+- meg
+- pytorch
+- neuroimaging
+license: mit
+datasets:
+- gabrycina/eeg2meg-tiny
+metrics:
+- mse
+---
 # EEG to MEG Prediction Model
 - 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)
+```

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   "num_epochs": 100,
   "learning_rate": 0.0001,
   "device": "mps",
-  "timestamp": "20250104_184834"
 }

   "num_epochs": 100,
   "learning_rate": 0.0001,
   "device": "mps",
+  "timestamp": "20250104_185119"
 }

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