The models presented in this represetory are mainly applied for biometric application. For more information on this work please read Graph Variational Auto-Encoder for Deriving EEG-based Graph Embedding.
This represetory contains the codes for previous work BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs. The code file is 'brainprint.py' and it uses graph features implemented in 'graphfeatures.py'. This model converts EEG signals into graph and manualy derive the features employing graph features such as minimum distance and clustering coefficients.
Two novel machine learning method for automaticily deriving EEG signals' features is presented. GCNN contains code for graph convolutional neural network for deriving brain graph features in supervised setting. GVAE is a corresponding code for a novel graph-based variational auto-encoder. The GVAE can dervie an unsupervised brain graph embedding.
All codes are written for Python 3 (https://www.python.org/) on Linux platform. The tensorflow version is 2.3.1.
The packages that are needed: tensorflow, os, sklearn, numpy, time, and networkx.
git clone git@github.com:Tinbeh97/Graph_ML.git
If you find this repository useful, please consider citing the following papers:
Understanding Power of Graph Convolutional Neural Network on Discriminating Human EEG Signal