okbalefthanded / aawedha

Deep Learning toolbox for EEG based Brain-Computer Interface signals decoding and benchmarking
GNU General Public License v3.0
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benchmark brain-computer-interface deep-learning eeg erp machine-learning motor-imagery ssvep

Aawedha

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


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:

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 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

Deep Learning Models

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

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