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

Overview of attention for article published in BMC Neuroscience, November 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
7 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
46 Mendeley
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Title
FACET – a “Flexible Artifact Correction and Evaluation Toolbox” for concurrently recorded EEG/fMRI data
Published in
BMC Neuroscience, November 2013
DOI 10.1186/1471-2202-14-138
Pubmed ID
Authors

Johann Glaser, Roland Beisteiner, Herbert Bauer, Florian Ph S Fischmeister

Abstract

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.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 1 2%
Austria 1 2%
Unknown 44 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 26%
Student > Ph. D. Student 11 24%
Professor > Associate Professor 4 9%
Student > Bachelor 4 9%
Student > Master 4 9%
Other 5 11%
Unknown 6 13%
Readers by discipline Count As %
Psychology 7 15%
Neuroscience 7 15%
Medicine and Dentistry 7 15%
Engineering 4 9%
Agricultural and Biological Sciences 4 9%
Other 6 13%
Unknown 11 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 November 2013.
All research outputs
#4,023,847
of 14,567,788 outputs
Outputs from BMC Neuroscience
#242
of 1,074 outputs
Outputs of similar age
#48,981
of 184,972 outputs
Outputs of similar age from BMC Neuroscience
#11
of 60 outputs
Altmetric has tracked 14,567,788 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,074 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 184,972 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.