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Abstract #2630

A Singular Value Shrinkage Approach to Remove Artifacts from Neuro-electrophysiology Data Recorded During fMRI at 16.4T

Corey Edward Cruttenden1, Wei Zhu1, Yi Zhang1, Xiao-Hong Zhu1, Rajesh Rajamani2, and Wei Chen1
1Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, 2Mechanical Engineering, University of Minnesota, Minneapolis, MN, United States

Acquiring neuro-electrophysiology signal simultaneously with fMRI is hindered by electromagnetic field interactions that generate artifacts, including fMRI gradient induced artifacts in the neuro-electrophysiology data. This abstract presents a novel method using a separation boundary on the singular value decomposition of the first difference of artifact-contaminated data to accurately reconstruct clean neural signals. The separation boundary can be estimated from a brief baseline recording period followed by simultaneous fMRI and neuro-electrophysiological data acquisition. The method is successfully demonstrated on neural recording data acquired simultaneously with time-series echo planar imaging at 16.4T.

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