GSoC 2021 project idea 7.3: Computational modelling of FNIRS and EEG with The Virtual Brain and Kernel Flow

The Virtual Brain (TVB) is a widely-used software library for connectome-based modelling of large-scale neural dynamics and neuroimaging data. To date, TVB models have mainly focused on brain dynamics as measured by fMRI, EEG, and LFPs. Functional Near Infrared Spectroscopy (FNIRS) is another noninvasive neuroimaging modality, which like fMRI measures haemodynamic signals reflecting neural activity, that has major potential in cognitive and clinical neuroscience.

The aim of this GSoC project will be to build out the modelling and analysis capacity of TVB for simulations of whole-head, high-resolution FNIRS signals, as well as concurrently recorded EEG. This shall include writing code implementing a temporal forward model for FNIRS-specific haemodynamic signals, a spatial forward model for optical sensor projection, and running simulations exploring dynamics of concurrent FNIRS-EEG activity. In terms of hardware, these development activities will be primarily focused on the Kernel Flow FNIRS+EEG system, that we will have access to and be using as part of the project.

Candidates should have experience with Python for scientific computing, and a strong interest in computational neuroscience and neuroimaging. Experience with one or more of the following is desirable: FNIRS/fMRI/EEG data analyses, neural mass modelling, numerical simulations and numerical optimization / model fitting problems in neuroscience or other domains. The project will provide excellent experience and training for students interested in pursuing research in human neuroimaging, theoretical/computational/cognitive/clinical neuroscience, and brain-computer interfaces.

Lead Mentor: John Griffiths @John_Griffiths
Co-Mentor: Randy McIntosh @rmcintosh

Tags: Python, TVB, Computational Neuroscience, Neural mass modelling, Connectome, Biophysics, FNIRS, EEG, electrophysiology


This project is quite interesting!
I wonder if the code will be wrapped up in a python package or integrated into a large codebase if the goals of this project have been achieved?
Also, any good pointers to get started with?


Great Qs.

Ultimately, we want the tools developed to be integrated into the larger code base.

However for period of this project that is not the priority; rather we will be aiming for a working prototype.

As to pointers, here are a few:

Keep the questions coming!

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Dear all who have expressed interested in the GSoC TVB-flow project,

We are now moving into the student applications phase of the GSoC timeline .

Those wishing to apply for the position will need to prepare and submit their application materials, which include their CV and a proposal statement.

The proposal should be based on the project brief at the top of this thread, and include the specific directions you would be interested to take, given your skills, knowledge and interests. You will be assessed on both your CV and the quality of your proposal statement.

You are also encouraged to seek feedback on your proposal statement. If you would like feedback on your proposal, please place it in a google doc and invite me (j.davidgriffiths@gmail) to edit asap. Please add comments with specific pointers on things you are unsure about and would like feedback on.

Good luck!