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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
13 news outlets
blogs
12 blogs
twitter
70 X users
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12 patents
facebook
1 Facebook page
wikipedia
3 Wikipedia pages
googleplus
7 Google+ users
reddit
1 Redditor
video
2 YouTube creators

Citations

dimensions_citation
541 Dimensions

Readers on

mendeley
1046 Mendeley
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5 CiteULike
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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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 70 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 1,046 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 37 4%
Germany 13 1%
Japan 9 <1%
United Kingdom 7 <1%
China 6 <1%
Canada 5 <1%
Switzerland 5 <1%
Spain 4 <1%
Portugal 3 <1%
Other 24 2%
Unknown 933 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 251 24%
Researcher 203 19%
Student > Bachelor 123 12%
Student > Master 119 11%
Professor > Associate Professor 57 5%
Other 173 17%
Unknown 120 11%
Readers by discipline Count As %
Engineering 246 24%
Agricultural and Biological Sciences 208 20%
Neuroscience 145 14%
Medicine and Dentistry 83 8%
Psychology 82 8%
Other 134 13%
Unknown 148 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 267. 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 19 January 2024.
All research outputs
#133,300
of 25,197,939 outputs
Outputs from Nature
#8,727
of 96,952 outputs
Outputs of similar age
#438
of 138,110 outputs
Outputs of similar age from Nature
#38
of 938 outputs
Altmetric has tracked 25,197,939 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 96,952 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 102.4. This one has done particularly well, scoring higher than 90% 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 138,110 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 938 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.