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Performance of the Emotiv Epoc headset for P300-based applications

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

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

twitter
3 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
144 Dimensions

Readers on

mendeley
422 Mendeley
citeulike
3 CiteULike
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Title
Performance of the Emotiv Epoc headset for P300-based applications
Published in
BioMedical Engineering OnLine, June 2013
DOI 10.1186/1475-925x-12-56
Pubmed ID
Authors

Matthieu Duvinage, Thierry Castermans, Mathieu Petieau, Thomas Hoellinger, Guy Cheron, Thierry Dutoit

Abstract

For two decades, EEG-based Brain-Computer Interface (BCI) systems have been widely studied in research labs. Now, researchers want to consider out-of-the-lab applications and make this technology available to everybody. However, medical-grade EEG recording devices are still much too expensive for end-users, especially disabled people. Therefore, several low-cost alternatives have appeared on the market. The Emotiv Epoc headset is one of them. Although some previous work showed this device could suit the customer's needs in terms of performance, no quantitative classification-based assessments compared to a medical system are available.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 422 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 2%
United Kingdom 5 1%
Brazil 3 <1%
Canada 2 <1%
Austria 2 <1%
Slovakia 1 <1%
Australia 1 <1%
Netherlands 1 <1%
Turkey 1 <1%
Other 8 2%
Unknown 390 92%

Demographic breakdown

Readers by professional status Count As %
Student > Master 96 23%
Student > Ph. D. Student 84 20%
Researcher 58 14%
Student > Bachelor 57 14%
Student > Doctoral Student 27 6%
Other 66 16%
Unknown 34 8%
Readers by discipline Count As %
Engineering 108 26%
Computer Science 89 21%
Psychology 48 11%
Neuroscience 29 7%
Medicine and Dentistry 20 5%
Other 76 18%
Unknown 52 12%

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 04 February 2017.
All research outputs
#2,211,616
of 9,006,411 outputs
Outputs from BioMedical Engineering OnLine
#63
of 452 outputs
Outputs of similar age
#30,941
of 126,157 outputs
Outputs of similar age from BioMedical Engineering OnLine
#1
of 13 outputs
Altmetric has tracked 9,006,411 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 452 research outputs from this source. They receive a mean Attention Score of 3.0. 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 126,157 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 74% of its contemporaries.
We're also able to compare this research output to 13 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.