Andreas Galka, Laith Hamid, Ulrich Stephani, and Michael Siniatchkin
Time Series Analysis, Filtering, State Space Modelling, Artifact Removal, Electroencephalogram
We propose a novel state space modelling approach to removing scanner-related artifacts from electroencephalograms recorded inside MR scanners. For this purpose, dynamical templates for the actual brain activity and the ballistocardiogram are obtained from a short piece of data recorded without fMRI scanning; dynamical templates for the scanner artifacts are obtained from data recorded during fMRI scanning. Finally the two sets of dynamical templates are merged. We compare our approach with Independent Component Analysis and find superior performance.
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