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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 33% |
| Japan | 1 | 17% |
| United States | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 50% |
| Members of the public | 3 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 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 | 48 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 26% |
| Researcher | 11 | 22% |
| Professor > Associate Professor | 4 | 8% |
| Student > Master | 4 | 8% |
| Student > Bachelor | 3 | 6% |
| Other | 6 | 12% |
| Unknown | 9 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 8 | 16% |
| Psychology | 8 | 16% |
| Medicine and Dentistry | 6 | 12% |
| Agricultural and Biological Sciences | 4 | 8% |
| Computer Science | 3 | 6% |
| Other | 8 | 16% |
| Unknown | 13 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 02 June 2022.
All research outputs
#5,379,457
of 25,632,496 outputs
Outputs from BMC Neuroscience
#229
of 1,303 outputs
Outputs of similar age
#47,213
of 228,619 outputs
Outputs of similar age from BMC Neuroscience
#6
of 44 outputs
Altmetric has tracked 25,632,496 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,303 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 82% 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 228,619 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.