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Improvement of classification accuracy in a phase-tagged steady-state visual evoked potential-based brain computer interface using multiclass support vector machine

Overview of attention for article published in BioMedical Engineering OnLine, May 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

news
1 news outlet

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
62 Mendeley
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Title
Improvement of classification accuracy in a phase-tagged steady-state visual evoked potential-based brain computer interface using multiclass support vector machine
Published in
BioMedical Engineering OnLine, May 2013
DOI 10.1186/1475-925x-12-46
Pubmed ID
Authors

Chia-Lung Yeh, Po-Lei Lee, Wei-Ming Chen, Chun-Yen Chang, Yu-Te Wu, Gong-Yau Lan

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Poland 1 2%
Denmark 1 2%
Italy 1 2%
Unknown 58 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 18%
Student > Bachelor 11 18%
Student > Ph. D. Student 10 16%
Researcher 9 15%
Student > Doctoral Student 3 5%
Other 6 10%
Unknown 12 19%
Readers by discipline Count As %
Engineering 18 29%
Medicine and Dentistry 8 13%
Neuroscience 7 11%
Computer Science 5 8%
Psychology 5 8%
Other 5 8%
Unknown 14 23%
Attention Score in Context

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 18 March 2022.
All research outputs
#4,306,235
of 23,371,053 outputs
Outputs from BioMedical Engineering OnLine
#100
of 832 outputs
Outputs of similar age
#36,479
of 196,927 outputs
Outputs of similar age from BioMedical Engineering OnLine
#1
of 14 outputs
Altmetric has tracked 23,371,053 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 832 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 85% 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 196,927 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 80% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.