Collection of 12 neural networks trained for motor imagery decoding along with evaluation results.
- Download and load in memory:
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
- Training dataset: Each model was trained on the dataset with corresponding name in the MOABB library (see datasets list).
- Details: For details on the training procedure, please refer to the poster here.
- 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 and the article Transfer Learning between Motor Imagery datasets using Deep Learning.
- Results: The evaluation results can be found under the
- Modedels training and results by: Pierre Guetschel
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