A web service for semi-automated conversion of raw imaging data to BIDS

TE-dependent analysis of multi-echo fMRI

This project aims to classify brain MRI images into four categories: Glioma, Meningioma, No tumor, and Pituitary tumor. It utilizes TensorFlow to build and train a convolutional neural network (CNN) for the task.

Inference-based time-resolved cortical stability and chaos analysis

Interface for using finite elements in inverse problems with complex domains

Workflows and interfaces for neuroimaging packages

Code for ICML 24 paper "Implicit Representations via Operator Learning"

BIDScoin converts your source-level neuroimaging data to BIDS

Framework for the reproducible processing of neuroimaging data with deep learning methods

fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.

Python package to access a cacophony of neuro-imaging file formats

A toolbox for comparing brain maps

Machine learning for NeuroImaging in Python

Magnetic resonance imaging and tractography with R

Still a work in progress.

Python API for Mentalab biosignal aquisition devices

A tutorial to implement a CNN for dementia brain imaging classification

lists datasets available in the PublicnEUro brain imaging repository

The project is used to do preprocessing on brain MR images by using Nipype.