Achieving optimal diagnostic accuracy with quantitative electrophysiology

This project will perfect current quantitative electrophysiology technique (qEEGt) based on multivariate spectral analysis of magnetic-electrophysiology recordings to provide sensitive biomarkers for its use in the Precision Brain Health, including diagnostic, prediction, and intervention. We will take advantage of the Bayesian statistics to produce novel solutions and solve the main unresolved problems of quantitative electroencephalography (qEEG): issues of reference electrode, elimination of recording artifacts; improved estimation of neural source connectivity; identification of the relevant co-variables that are co-founding factors for prediction; identification of the most relevant features for detecting normal and abnormal brain states; integration of qEEG measures into disease progression model of aging to provide predictions of individualized brain health trajectories. The developed approach will be applied into the analysis of large data-sets of Alzheimer disease. This project will contribute to the Health Brain Health Life (HBHL) project and the joint Canada-Cuba-China brain imaging project as well.

Faculty Supervisor:

Alan Evans

Student:

Partner:

The University of Electronic Science and Technology of China

Discipline:

Engineering

Sector:

Education

University:

McGill University

Program: