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Active tactile exploration using a brain–machine–brain interface

Overview of attention for article published in Nature, October 2011
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
10 news outlets
blogs
13 blogs
twitter
71 tweeters
patent
3 patents
facebook
1 Facebook page
wikipedia
1 Wikipedia page
googleplus
2 Google+ users
reddit
1 Redditor
video
2 video uploaders

Citations

dimensions_citation
399 Dimensions

Readers on

mendeley
887 Mendeley
citeulike
5 CiteULike
Title
Active tactile exploration using a brain–machine–brain interface
Published in
Nature, October 2011
DOI 10.1038/nature10489
Pubmed ID
Authors

Joseph E. O’Doherty, Mikhail A. Lebedev, Peter J. Ifft, Katie Z. Zhuang, Solaiman Shokur, Hannes Bleuler, Miguel A. L. Nicolelis

Abstract

Brain-machine interfaces use neuronal activity recorded from the brain to establish direct communication with external actuators, such as prosthetic arms. It is hoped that brain-machine interfaces can be used to restore the normal sensorimotor functions of the limbs, but so far they have lacked tactile sensation. Here we report the operation of a brain-machine-brain interface (BMBI) that both controls the exploratory reaching movements of an actuator and allows signalling of artificial tactile feedback through intracortical microstimulation (ICMS) of the primary somatosensory cortex. Monkeys performed an active exploration task in which an actuator (a computer cursor or a virtual-reality arm) was moved using a BMBI that derived motor commands from neuronal ensemble activity recorded in the primary motor cortex. ICMS feedback occurred whenever the actuator touched virtual objects. Temporal patterns of ICMS encoded the artificial tactile properties of each object. Neuronal recordings and ICMS epochs were temporally multiplexed to avoid interference. Two monkeys operated this BMBI to search for and distinguish one of three visually identical objects, using the virtual-reality arm to identify the unique artificial texture associated with each. These results suggest that clinical motor neuroprostheses might benefit from the addition of ICMS feedback to generate artificial somatic perceptions associated with mechanical, robotic or even virtual prostheses.

Twitter Demographics

The data shown below were collected from the profiles of 71 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 887 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 39 4%
Germany 15 2%
Japan 9 1%
United Kingdom 7 <1%
China 6 <1%
Canada 5 <1%
Switzerland 5 <1%
Portugal 4 <1%
Spain 4 <1%
Other 24 3%
Unknown 769 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 229 26%
Researcher 191 22%
Student > Master 109 12%
Student > Bachelor 107 12%
Professor > Associate Professor 55 6%
Other 149 17%
Unknown 47 5%
Readers by discipline Count As %
Engineering 221 25%
Agricultural and Biological Sciences 207 23%
Neuroscience 110 12%
Psychology 79 9%
Medicine and Dentistry 78 9%
Other 117 13%
Unknown 75 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 250. 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 28 October 2019.
All research outputs
#66,168
of 15,625,083 outputs
Outputs from Nature
#6,296
of 75,273 outputs
Outputs of similar age
#291
of 104,301 outputs
Outputs of similar age from Nature
#35
of 943 outputs
Altmetric has tracked 15,625,083 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 75,273 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 84.7. This one has done particularly well, scoring higher than 91% 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 104,301 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 99% of its contemporaries.
We're also able to compare this research output to 943 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 96% of its contemporaries.