Edit model card

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

How to Get Started with the Model

  • Download and load in memory:
import pickle

# download the model from the hub:
path_kwargs = hf_hub_download(
path_params = hf_hub_download(
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 Details

  • 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 results/ folder.

Model Card Authors

  • Modedels training and results by: Pierre Guetschel
Downloads last month
Hosted inference API

Unable to determine this model’s pipeline type. Check the docs .