| | --- |
| | 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. |
| |
|
| | ## 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 |
| |
|
| | - **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 |
| |
|
| | - **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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