Removal of FMRI environment artifacts from EEG data using optimal basis sets.

Abstract:

:The combination of functional magnetic resonance imaging (FMRI) and electroencephalography (EEG) has received much recent attention, since it potentially offers a new tool for neuroscientists that makes simultaneous use of the strengths of the two modalities. However, EEG data collected in such experiments suffer from two kinds of artifact. First, gradient artifacts are caused by the switching of magnetic gradients during FMRI. Second, ballistocardiographic (BCG) artifacts related to cardiac activities further contaminate the EEG data. Here we present new methods to remove both kinds of artifact. The methods are based primarily on the idea that temporal variations in the artifacts can be captured by performing temporal principal component analysis (PCA), which leads to the identification of a set of basis functions which describe the temporal variations in the artifacts. These basis functions are then fitted to, and subtracted from, EEG data to produce artifact-free results. In addition, we also describe a robust algorithm for the accurate detection of heart beat peaks from poor quality electrocardiographic (ECG) data that are collected for the purpose of BCG artifact removal. The methods are tested and are shown to give superior results to existing methods. The methods also demonstrate the feasibility of simultaneous EEG/FMRI experiments using the relatively low EEG sampling frequency of 2048 Hz.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Niazy RK,Beckmann CF,Iannetti GD,Brady JM,Smith SM

doi

10.1016/j.neuroimage.2005.06.067

keywords:

subject

Has Abstract

pub_date

2005-11-15 00:00:00

pages

720-37

issue

3

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(05)00472-6

journal_volume

28

pub_type

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