Submitting a Project

We invite submissions of project ideas, suggestions, and resources that may be useful for others to this Brainhack project page. To submit something, please send an email to with "Brainhack Global 2018 Project and Resource Submission" as the subject and the following information in the body. Your submission is likely to be edited for brevity and content.

Project Listing

Enter a search term below to filter the projects and resources:


ASL pipeline in Anima

The goal is to build a complete pipeline for quantifying cerebral blood flow (and possibly other perfusion maps) from ASL and M0 images using Anima, an open source software for medical image processing. The project consists in combining different stages of ASL preprocessing images using functions already available in Anima.

Anima workflows in Nipype

Nipype looks like a great framework to define small processing bricks and automatically assemble them to form pipelines. Other well known softwares like Nifty* or ANTS already have their interfaces. The idea of this project is to study how workflows and interfaces are made in Nipype through the example of creating some from the Anima open-source software.

EEG neurofeedback with MENSIA Modulo

We have a new 8 channels EEG cap from MENSIA Modulo, and we would like to see how it works in a first time. Then we want to acquire several neurofeedback sessions, convert the data to a matlab formating, before processing the EEG signals and modelling the neurofeedback scores to improve the NF filtering.
Contact: Claire Cury or Giulia Lioi

Python-only fMRI processing

This project aims at processing a task-based fMRI dataset with Python tools only (Nipype, nistats, nilearn, nibabel etc...). The pipeline shall include the following steps: reading a BIDS format dataset, performing the pre-processing stages (slice timing, realignment, coregistration, normalisation), specifying design matrices (GLM 1st and 2nd level) and generating a results report.
Contact: Quentin Duché

fMRI processing roundtable

What you always wanted to try and never find the time to, starting with for example denoising, modelling for task based fMRI, whole brain or ROI analysis, correction for multiple comparisons, DCM.
Contact: Quentin Duché

BIDS importer/exporter for Shanoir

We will develop a new feature for the brain imaging database Shanoir to allow for import of datasets organised using the Brain Imaging Data Structure (BIDS). We will use public datasets from OpenfMRI as test data.
Contact: Michael Kain

Deep Learning Playground

A practical on how to use Google’s Tensor Flow framework (i.e. Tensor Flow in Python) to create artificial neural networks for deep learning. We will try to achieve an easy understanding of the complexities of the Tensor Flow framework, build a network from scratch with Python, and - why not - use a convolutional neural network for image processing.


MNE BIDS is a python package to automatically converting existing files into BIDS compatible datasets.
Contact: Mainak Jas

BCI with Emotiv

The goal of the project would be to investigate the capability of the Emotiv EPOC 16-channel portable EEG to design a BCI applications. We can start with a simple binary classification case and let's see where we finish!
Contact: Pierre Maurel

Automatic quality control on large dataset

We would like to address the relation between data quality and error on quantitative biomarkers by looking at large databases. We propose to discuss how to define quality indexes (QI), and how to infer the relation between QI and the bias or the variability of different biomarker. We are open to collaboration to work on the large database acquired at CENIR (~ 17000 T1w scans with a consequent proportion of rescan due to bad quality of the first acquisition).

An fMRI meta-analysis

Little is known about the brain mechanisms of recovery after stroke. Here we will focus on two rehabilitation methods of the upper limb: constraint induced movement therapy and motor imagery. After reviewing the literature, we selected 32 articles. We want to perform a meta-analysis on fMRI data to understand the cerebral mechanisms of these rehabilitation programs, using tools such as NeuroVault, Brainspell and/or NIDM-Results.
Contact: Simon Butet
ADHD-200 Preprocessed

ADHD-200 Preprocessed

The ADHD-200 Preprocessed repository contains functional and structural data on individuals with ADHD and healthy controls that have been processed using three different pipelines.
Contact: Cameron Craddock