I-EEG: health application for semi-automated iEEG analysis and viewing in a clinical and research environment
Mentors: Maya Aderka maya.aderka@mail.mcgill.ca, Suresh Krishna suresh.krishna@mcgill.ca, Elie Bou Assi elie.bou.assi.chum@ssss.gouv.qc.ca
Skill level: Intermediate – Advanced
Required Skills: Fluency in at least one of Python or Matlab (Python preferred, and ideally with reasonable ability in MATLAB). Experience with (bio)signal processing and with front-end development preferred.
Time commitment: Full time (350 hours)
Forum for discussion
About:
There is an acute need for (open-source) software to handle human intracranial neural recordings, usually from patients who are undergoing diagnostic intracranial EEG (iEEG) recordings for epilepsy treatment. Such software would provide an integrated viewer, implement major existing (semi-)automated algorithms for seizure-onset zone definition, seizure prediction and surgical outcome prognostication. This is a fairly new project, that the GSoC contributor will build on, with help and mentorship from us.
Aims:
This year, the project will aim to implement additional key published iEEG algorithms, improve the front-end for visualization, create a pipeline that allows the incoporation of expert human input to fine-tune the automated analyses, and to build a framework for testing/comparison of different algorithms and human-in-the-loop pipelines to each other.
Website: https://m2b3.github.com/ieeg
Tech keywords: Health ML/AI, Epilepsy, Neuroscience, Biosignals, Neuroinformatics