User-friendly open-source pipeline for anatomically precise analysis of single-trial M/EEG

The overall goal of this project is developing data analysis tools to unveil how the human brain encodes complex and natural stimuli, using electrophysiological datasets (primarily MEG and EEG). The approach is based on the neuro-current response function method (introduced in Das et al., 2020). Planned work includes theoretical development as well as implementation of a user-friendly analysis pipeline.

The project is led by Christian Brodbeck, PhD (McMaster) and Proloy Das, PhD (Stanford). Collaborators include Patrick L. Purdon, PhD (Stanford), Isabelle Buard, PhD (UofColorado), Monty Escabí, PhD (UConn) and Steven Stufflebeam, MD (MGH).

Development will primarily be using public M/EEG datasets, and provides room to work on different topic areas, such as auditory and speech processing, sensory-motor processing, external stimulus processing in patients undergoing anesthesia, etc.

This project is funded by NIH.