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Future developments in brain-machine interface research

Overview of attention for article published in Clinics, January 2011
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#13 of 483)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
2 news outlets
blogs
2 blogs
twitter
4 tweeters
patent
2 patents
video
1 video uploader

Citations

dimensions_citation
74 Dimensions

Readers on

mendeley
257 Mendeley
citeulike
1 CiteULike
Title
Future developments in brain-machine interface research
Published in
Clinics, January 2011
DOI 10.1590/s1807-59322011001300004
Pubmed ID
Authors

Mikhail A. Lebedev, Andrew J. Tate, Timothy L. Hanson, Zheng Li, Joseph E. O'Doherty, Jesse A. Winans, Peter J. Ifft, Katie Z. Zhuang, Nathan A. Fitzsimmons, David A. Schwarz, Andrew M. Fuller, Je Hi An, Miguel A. L. Nicolelis

Abstract

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 257 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 4%
Brazil 4 2%
United Kingdom 2 <1%
Bangladesh 2 <1%
Mexico 1 <1%
China 1 <1%
Germany 1 <1%
Unknown 235 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 72 28%
Student > Master 49 19%
Researcher 38 15%
Student > Bachelor 35 14%
Student > Postgraduate 14 5%
Other 36 14%
Unknown 13 5%
Readers by discipline Count As %
Engineering 93 36%
Agricultural and Biological Sciences 47 18%
Neuroscience 30 12%
Medicine and Dentistry 26 10%
Computer Science 13 5%
Other 25 10%
Unknown 23 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 34. 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 10 September 2019.
All research outputs
#581,812
of 14,865,393 outputs
Outputs from Clinics
#13
of 483 outputs
Outputs of similar age
#8,660
of 188,603 outputs
Outputs of similar age from Clinics
#1
of 10 outputs
Altmetric has tracked 14,865,393 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 483 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. 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 188,603 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 95% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them