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Decoding spectrotemporal features of overt and covert speech from the human cortex

Overview of attention for article published in Frontiers in Neuroengineering, May 2014
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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 (98th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

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5 news outlets
blogs
4 blogs
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39 X users
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6 Facebook pages
wikipedia
1 Wikipedia page
reddit
1 Redditor
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2 Q&A threads

Citations

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167 Dimensions

Readers on

mendeley
325 Mendeley
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Title
Decoding spectrotemporal features of overt and covert speech from the human cortex
Published in
Frontiers in Neuroengineering, May 2014
DOI 10.3389/fneng.2014.00014
Pubmed ID
Authors

Stéphanie Martin, Peter Brunner, Chris Holdgraf, Hans-Jochen Heinze, Nathan E. Crone, Jochem Rieger, Gerwin Schalk, Robert T. Knight, Brian N. Pasley

Abstract

Auditory perception and auditory imagery have been shown to activate overlapping brain regions. We hypothesized that these phenomena also share a common underlying neural representation. To assess this, we used electrocorticography intracranial recordings from epileptic patients performing an out loud or a silent reading task. In these tasks, short stories scrolled across a video screen in two conditions: subjects read the same stories both aloud (overt) and silently (covert). In a control condition the subject remained in a resting state. We first built a high gamma (70-150 Hz) neural decoding model to reconstruct spectrotemporal auditory features of self-generated overt speech. We then evaluated whether this same model could reconstruct auditory speech features in the covert speech condition. Two speech models were tested: a spectrogram and a modulation-based feature space. For the overt condition, reconstruction accuracy was evaluated as the correlation between original and predicted speech features, and was significant in each subject (p < 10(-5); paired two-sample t-test). For the covert speech condition, dynamic time warping was first used to realign the covert speech reconstruction with the corresponding original speech from the overt condition. Reconstruction accuracy was then evaluated as the correlation between original and reconstructed speech features. Covert reconstruction accuracy was compared to the accuracy obtained from reconstructions in the baseline control condition. Reconstruction accuracy for the covert condition was significantly better than for the control condition (p < 0.005; paired two-sample t-test). The superior temporal gyrus, pre- and post-central gyrus provided the highest reconstruction information. The relationship between overt and covert speech reconstruction depended on anatomy. These results provide evidence that auditory representations of covert speech can be reconstructed from models that are built from an overt speech data set, supporting a partially shared neural substrate.

X Demographics

X Demographics

The data shown below were collected from the profiles of 39 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 325 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 <1%
Portugal 1 <1%
Malaysia 1 <1%
Chile 1 <1%
France 1 <1%
Germany 1 <1%
Korea, Republic of 1 <1%
New Zealand 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 313 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 76 23%
Researcher 60 18%
Student > Master 44 14%
Student > Bachelor 36 11%
Student > Doctoral Student 16 5%
Other 46 14%
Unknown 47 14%
Readers by discipline Count As %
Neuroscience 66 20%
Engineering 62 19%
Agricultural and Biological Sciences 30 9%
Psychology 29 9%
Computer Science 28 9%
Other 45 14%
Unknown 65 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 109. 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 27 May 2022.
All research outputs
#400,526
of 26,071,599 outputs
Outputs from Frontiers in Neuroengineering
#2
of 82 outputs
Outputs of similar age
#3,320
of 242,950 outputs
Outputs of similar age from Frontiers in Neuroengineering
#1
of 10 outputs
Altmetric has tracked 26,071,599 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 82 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. 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 242,950 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 98% 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