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Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, January 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#41 of 1,388)
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
2 news outlets
twitter
7 X users
patent
2 patents
reddit
1 Redditor

Citations

dimensions_citation
202 Dimensions

Readers on

mendeley
300 Mendeley
citeulike
2 CiteULike
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Title
Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors
Published in
Journal of NeuroEngineering and Rehabilitation, January 2012
DOI 10.1186/1743-0003-9-5
Pubmed ID
Authors

Lun-De Liao, Chi-Yu Chen, I-Jan Wang, Sheng-Fu Chen, Shih-Yu Li, Bo-Wei Chen, Jyh-Yeong Chang, Chin-Teng Lin

Abstract

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 300 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
Netherlands 1 <1%
Cuba 1 <1%
Finland 1 <1%
United Kingdom 1 <1%
Canada 1 <1%
Brazil 1 <1%
Argentina 1 <1%
Mexico 1 <1%
Other 2 <1%
Unknown 287 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 66 22%
Student > Master 45 15%
Researcher 44 15%
Student > Bachelor 28 9%
Student > Doctoral Student 15 5%
Other 42 14%
Unknown 60 20%
Readers by discipline Count As %
Engineering 87 29%
Computer Science 54 18%
Neuroscience 19 6%
Agricultural and Biological Sciences 14 5%
Psychology 13 4%
Other 43 14%
Unknown 70 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 24 January 2023.
All research outputs
#1,338,549
of 24,927,532 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#41
of 1,388 outputs
Outputs of similar age
#9,077
of 257,622 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#1
of 21 outputs
Altmetric has tracked 24,927,532 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,388 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done particularly well, scoring higher than 97% 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 257,622 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 21 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 99% of its contemporaries.