Aawedha (عاودها means repeate it or do it again in Algerian arabic) is a deep learning learning package based on Keras with Tensorflow backend, for EEG based Brain-Computer Interface (BCI) decoding research and application.
Compatible with Python 3.6 and above
The main goal for this package is to provide a flexible and complete analysis and benchmarking tool for Deep Learning research in BCI.
Aawedha provides a complete set of operations from raw data preprocessing to model evaluation and results visualization. A regular workflow using this package consists of 5 instructions:
- Create a dataset: preprocess raw data to create epoched EEG trials (run once)
- Define an Evaluation : Single subject or Cross Subject analysis with the data and model.
- Generate a random data split.
- Run evaluation : train and test model.
- Visualize the results and what the model has learnt.
The tables below show the available datasets and models, for a detailed tutorial on running the evaluations follow the colaboratory notebook in the examples folder.
| Datasets | Paradigm | Participants(subjects) |
|---|---|---|
| BCI Competetion IV 2a | Motor Imagery | 9 |
| Exoskleton | SSVEP | 12 |
| Freiburg Online ERP | ERP | 13 |
| Inria ERN | ErrP | 26 |
| Laresi Hyrbid | Hybrid ERP/SSVEP | 1 |
| Physionet_MI | Motor Imagery | 109 |
| San Diego | SSVEP | 10 |
| Tsinghua | SSVEP | 35 |
| Title | Paradigm | Architecture |
|---|---|---|
| EEGNET | Motor Imagery / ERP/Errp | ConvNet |
| EEGNet SSVEP | SSVEP | ConvNet |
| DeepConvNet/ ShallowConvNet | Motor Imagery / ERP/Errp | ConvNet |
| 1DCSU | SSVEP | ConvNet |
| PodNet | SSVEP | ConvNet |
| KoreaU CNN | SSVEP | ConvNet |
| Xu_Jiang CNN | SSVEP | ConvNet |
First, clone Aawedha using git:
git clone https://github.com/okbalefthanded/aawedha.git
Then, cd to the Aawedha folder, install requirements using pip then proceed to package setup:
cd aawedha
pip install -r requirements.txt
python setup.py install
Follow the colab notebooks in /examples