# 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


{"license": "mit"}
license:mit region:us
TAGS
#license-mit #region-us 

# 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 by  Lawhern et. al (2018).


## How to Get Started with the Model

- Download and load in memory:

- 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.

## 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 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


# 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 by  Lawhern et. al (2018). ## How to Get Started with the Model

- Download and load in memory:

- 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. ## 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 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
TAGS
#license-mit #region-us 
 # 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 by  Lawhern et. al (2018). ## How to Get Started with the Model

- Download and load in memory:

- 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. ## 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 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