Machine learning for NeuroImaging in Python

Muse 2016 EEG Headset JavaScript Library (using Web Bluetooth)

A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.

Graph theory analysis of brain MRI data

Frequency domain estimation and functional and directed connectivity analysis tools for electrophysiological data

A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning

Regression Graph Neural Network (regGNN) for cognitive score prediction.

Python Machine Learning Toolbox for Brain Network Classification. Source codes are included of the top 20 teams in the Kaggle competition.

The Brain Activity Flow ("Actflow") Toolbox. Tools to quantify the relationship between connectivity and task activity through network simulations and machine learning prediction. Helps determine how connections contribute to specific brain functions.

GNU Octave library for connectivity analysis in large fMRI datasets

How to fuse a population of graphs into a single one using graph neural networks?

Pythonic implementation of the Phase Transfer Entropy method using NumPy and SciPy

MultiGraphGAN for predicting multiple target graphs from a source graph using geometric deep learning.

Brain Network Toolbox

Graph SuperResolution Network using geometric deep learning.

HCAE (HyperConnectome AutoEncoder) for brain state identification.

Essential Motor Cortex Signal Processing MATLAB Toolbox which implements various methods for three major aspects of investigating human motor cortex from Neuroscience view point: (1) ERP estimation and quantification, (2) Cortical Functional Connectivity analysis and (3) EMG quantification

Brain Analysis using Graph Theory

MGN-Net: A novel Graph Neural Network for integrating heterogenous graph population derived from multiple sources.

Predicting multigraph brain population from a single graph