A dynamic connectome mapping module in python.

Easy and comprehensive assessment of predictive power, with support for neuroimaging features

Framework for Information Theoretical analysis of Electrophysiological data and Statistics

A large-scale spiking model of the vision-related areas of macaque cortex.

Small Animal Magnetic Resonance Imaging via Python.

Improving autism identification with multisite data via site-dependence minimisation and second-order functional connectivity (TMI, 2022)

Dynamical graphical models for multivariate time series data to estimate directed dynamic relationships in networks.

No description provided.

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

Collection of Matlab functions for denoising fMRI data

Python library to compute functional connectivity measures from EEG

The neural basis of intelligence in fine-grained cortical topographies

Supporting code for https://www.biorxiv.org/content/10.1101/796714v4

Connectome-based Hopfiled Neural Networks (fcHNN)

Functional connectivity and brain network analysis for motor imagery data in stroke patients

Project repository of the Matters Arising commentary "Multivariate BWAS can be replicable with moderate sample sizes in some cases""

Methods for estimating time-varying functional connectivity (TVFC)

Leading Eigenvector Dynamics Analysis Matlab Toolbox

EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters

TMFC toolbox - a new SPM toolbox for whole-brain task-modulated functional connectivity (TMFC) analysis with user-friendly graphical interface.

Track and segment the dynamics of brain connectivity networks

Brainstorm plug-ins for MEG and EEG source imaging, including (1) maximum contrast beamformer (MCB) for the use in localization of brain sources, (2) spatiotemporal imaging of linearly-related source components (SILSC) for the use in identification of sources linearly correlated to the activity of a seed, and (3) beamformer-based imaging of phase-amplitude coupling (BIPAC) for the use in identification of sources coupled to seed activity.

Materials and source code for my MSc thesis: "Studying Network Variants With Electroencephalography"

A machine learning model to diagnose ADHD, using resting-state fMRI data.

Work did at the end of my internship, based on Nastaran Hamedi et al. Detecting ADHD Based on Brain Functional Connectivity Using Resting-State MEG Signals

Machine learning pipelines with K-fold cross-validation library for FC data

For local SC-FC subgraph extraction and counterfactual explanation.

a simple C++ graph analysis library

Code to run and analyze fMRI study of somatosensory detection task

Benchmarks for functional connectivity estimators and FCEst Python package