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https://github.com/okbalefthanded/aawedha
Deep Learning toolbox for EEG based Brain-Computer Interface signals decoding and benchmarking
https://github.com/okbalefthanded/aawedha
benchmark brain-computer-interface deep-learning eeg erp machine-learning motor-imagery ssvep
Last synced: 5 days ago
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Deep Learning toolbox for EEG based Brain-Computer Interface signals decoding and benchmarking
- Host: GitHub
- URL: https://github.com/okbalefthanded/aawedha
- Owner: okbalefthanded
- License: gpl-3.0
- Created: 2019-02-07T10:07:17.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-03-20T22:43:32.000Z (10 months ago)
- Last Synced: 2024-05-15T09:46:48.940Z (8 months ago)
- Topics: benchmark, brain-computer-interface, deep-learning, eeg, erp, machine-learning, motor-imagery, ssvep
- Language: Python
- Homepage:
- Size: 983 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Aawedha
***Aawedha*** (*عاودها* means repeate it or do it again in Algerian arabic) is a deep learning learning package based on [Keras](https://www.tensorflow.org/guide/keras/overview) with [Tensorflow](https://www.tensorflow.org/guide) backend, for EEG based Brain-Computer Interface (BCI) decoding research and application.
Compatible with **Python 3.6 and above**
---
## Motivation
The main goal for this package is to provide a flexible and complete analysis and benchmarking tool for Deep Learning research in BCI.
---
## Features
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.
### Data
| Datasets | Paradigm | Participants(subjects) |
| ------------- |:-------------:| :-----:|
| [BCI Competetion IV 2a](http://www.bbci.de/competition/iv/) | Motor Imagery | 9 |
| [Exoskleton](https://github.com/sylvchev/dataset-ssvep-exoskeleton) | SSVEP | 12 |
| [Freiburg Online ERP](https://zenodo.org/record/192684) | ERP | 13 |
| [Inria ERN](https://www.kaggle.com/c/inria-bci-challenge) | ErrP | 26 |
| [Laresi Hyrbid]() | Hybrid ERP/SSVEP | 1 |
| [Physionet_MI](https://physionet.org/content/eegmmidb/1.0.0/) | Motor Imagery | 109 |
| [San Diego](ftp://sccn.ucsd.edu/pub/cca_ssvep) | SSVEP | 10 |
| [Tsinghua](http://bci.med.tsinghua.edu.cn/download.html) | SSVEP | 35 |
### Deep Learning Models
| Title | Paradigm | Architecture |
| ------------- |:-------------:| -----:|
| [EEGNET](https://github.com/vlawhern/arl-eegmodels) | Motor Imagery / ERP/Errp | ConvNet |
| [EEGNet SSVEP](https://github.com/vlawhern/arl-eegmodels) | SSVEP | ConvNet |
| [DeepConvNet/ ShallowConvNet](https://github.com/TNTLFreiburg/braindecode) | Motor Imagery / ERP/Errp | ConvNet |
| [1DCSU](https://arxiv.org/abs/1805.04157) | SSVEP | ConvNet |
| [PodNet](http://dro.dur.ac.uk/27626/) | SSVEP | ConvNet |
| [KoreaU CNN](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0172578) | SSVEP | ConvNet |
| [Xu_Jiang CNN](https://ieeexplore.ieee.org/document/8708243) | SSVEP | ConvNet |
---
## Installation
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
```
---
## Usage
```
Follow the colab notebooks in /examples
```
---
## Citation
---
## Acknowledgment