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 Postdoctoral Fellows will work as a part of a multidisciplinary team including researchers with Engineering, Neuroscience and Medicine background. Candidates will work primarily with Christian Brodbeck, PhD (McMaster), Proloy Das, PhD (Stanford) and Patrick L. Purdon, PhD (Stanford), but may also consult with a team with diverse topic interests in neuroscience, including Isabelle Buard, PhD (UofColorado), Monty Escabí, PhD (UConn) and Steven Stufflebeam, MD (MGH).

We are searching for applicants interested in theory and algorithm development, as well as the development of a versatile data analysis pipeline with user-friendly front-end and documentation. 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.

Ideal candidates should have experience and interests in the following areas: signal processing; system identification; computational neuroscience; Python development. Prior experience with MEG and/or EEG is not required but would be a strong asset.

Formal announcement to follow. Feel free to reach out to us for further information (brodbecc@mcmaster.ca, proloy@stanford.edu, ppurdon@stanford.edu)

This project is funded by NIH.