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FACET – a “Flexible Artifact Correction and Evaluation Toolbox” for concurrently recorded EEG/fMRI data

Johann Glaser1, Roland Beisteiner23, Herbert Bauer1 and Florian Ph.S Fischmeister23*

Author affiliations

1 Biological Psychology Unit, Faculty of Psychology, University of Vienna, Liebiggasse 5, A-1010 Vienna, Austria

2 High Field MR Centre of Excellence, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria

3 Study Group Clinical fMRI, Department of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, A-1090 Vienna, Austria

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Citation and License

BMC Neuroscience 2013, 14:138  doi:10.1186/1471-2202-14-138

Published: 9 November 2013

Abstract

Background

In concurrent EEG/fMRI recordings, EEG data are impaired by the fMRI gradient artifacts which exceed the EEG signal by several orders of magnitude. While several algorithms exist to correct the EEG data, these algorithms lack the flexibility to either leave out or add new steps. The here presented open-source MATLAB toolbox FACET is a modular toolbox for the fast and flexible correction and evaluation of imaging artifacts from concurrently recorded EEG datasets. It consists of an ANALYSIS, a CORRECTION and an EVALUATION framework allowing the user to choose from different artifact correction methods with various pre- and post-processing steps to form flexible combinations. The quality of the chosen correction approach can then be evaluated and compared to different settings.

Results

FACET was evaluated on a dataset provided with the FMRIB plugin for EEGLAB using two different correction approaches: Averaged Artifact Subtraction (AAS, Allen et al., NeuroImage 12(2):230–239, 2000) and the FMRI Artifact Slice Template Removal (FASTR, Niazy et al., NeuroImage 28(3):720–737, 2005). Evaluation of the obtained results were compared to the FASTR algorithm implemented in the EEGLAB plugin FMRIB. No differences were found between the FACET implementation of FASTR and the original algorithm across all gradient artifact relevant performance indices.

Conclusion

The FACET toolbox not only provides facilities for all three modalities: data analysis, artifact correction as well as evaluation and documentation of the results but it also offers an easily extendable framework for development and evaluation of new approaches.