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---
license: mit
---
# Model Card for pre-trained EEGNet models on mental imagery datasets
Collection of 12 neural networks trained for motor imagery decoding along with evaluation results.
## Model Details
- **Architecture:** [EEGNetv4](https://braindecode.org/stable/generated/braindecode.models.EEGNetv4.html) by [Lawhern et. al (2018)](https://doi.org/10.1088/1741-2552/aace8c).
## How to Get Started with the Model
- **Download and load in memory:**
```python
import pickle
# download the model from the hub:
path_kwargs = hf_hub_download(
repo_id='PierreGtch/EEGNetv4',
filename='EEGNetv4_Lee2019_MI/kwargs.pkl',
)
path_params = hf_hub_download(
repo_id='PierreGtch/EEGNetv4',
filename='EEGNetv4_Lee2019_MI/model-params.pkl',
)
with open(path_kwargs, 'rb') as f:
kwargs = pickle.load(f)
module_cls = kwargs['module_cls']
module_kwargs = kwargs['module_kwargs']
# load the model with pre-trained weights:
torch_module = module_cls(**module_kwargs)
```
- **Details:** more details and potential use-case scenarios can be found in the notebook [here](https://neurotechlab.socsci.ru.nl/resources/pretrained_imagery_models/)
## Training Details
- **Training dataset:** Each model was trained on the dataset with corresponding name in the MOABB library (see [datasets list](https://neurotechx.github.io/moabb/dataset_summary.html#motor-imagery)).
- **Details:** For details on the training procedure, please refer to the poster [here](https://neurotechlab.socsci.ru.nl/resources/pretrained_imagery_models/).
## Evaluation
- **Cross-dataset transfer:** The transfer abilities of the models was tested on the same datasets as for training.
- **Details:** The evaluation procedure can be found in the poster [here](https://neurotechlab.socsci.ru.nl/resources/pretrained_imagery_models/) and the article *Transfer Learning between Motor Imagery datasets using Deep Learning*.
- **Results:** The evaluation results can be found under the [`results/`](https://huggingface.co/PierreGtch/EEGNetv4/tree/main/results) folder.
## Model Card Authors
- **Modedels training and results by:** Pierre Guetschel
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