Multivariate statistical algorithms for optimal signal quality in simultaneous EEG-fMRI

When EEG is recorded inside an MRI scanner, the MRI equipment causes extra signals to show up in the EEG data. This is a bit like trying to understand someone's voice when they're using a microphone that's creating static and loud feedback. You would need computer software to separate the two sounds. In studies measuring EEG in an MRI scanner, we have the challenge of distinguishing between the part of the EEG caused by the brain and the part of the EEG caused by the scanner. We propose to use our mathematical expertise to find the most effective way of distinguishing between these two parts of the EEG data. Our mathematical protocols will be specialized for the extra signals created by a GE Discovery MR750 3.0 Tesla MRI scanner.

Intern: 
Jennifer Whitman
Faculty Supervisor: 
Dr. Todd Woodward
Project Year: 
2014
Province: 
British Columbia
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