| --- |
| license: mit |
| --- |
| # Model Card for pre-trained EEGNet models on mental imagery datasets |
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| Collection of 12 neural networks trained for motor imagery decoding along with evaluation results. |
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| ## Model Details |
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| - **Architecture:** [EEGNetv4](https://braindecode.org/stable/generated/braindecode.models.EEGNetv4.html) by [Lawhern et. al (2018)](https://doi.org/10.1088/1741-2552/aace8c). |
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| ## How to Get Started with the Model |
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| - **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/) |
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| ## Training Details |
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| - **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/). |
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| ## Evaluation |
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| - **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. |
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| ## Model Card Authors |
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| - **Modedels training and results by:** Pierre Guetschel |
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