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Decoding onset and direction of movements using Electrocorticographic (ECoG) signals in humans

Overview of attention for article published in Frontiers in Neuroengineering, January 2012
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Title
Decoding onset and direction of movements using Electrocorticographic (ECoG) signals in humans
Published in
Frontiers in Neuroengineering, January 2012
DOI 10.3389/fneng.2012.00015
Pubmed ID
Authors

Zuoguan Wang, Aysegul Gunduz, Peter Brunner, Anthony L. Ritaccio, Qiang Ji, Gerwin Schalk

Abstract

Communication of intent usually requires motor function. This requirement can be limiting when a person is engaged in a task, or prohibitive for some people suffering from neuromuscular disorders. Determining a person's intent, e.g., where and when to move, from brain signals rather than from muscles would have important applications in clinical or other domains. For example, detection of the onset and direction of intended movements may provide the basis for restoration of simple grasping function in people with chronic stroke, or could be used to optimize a user's interaction with the surrounding environment. Detecting the onset and direction of actual movements are a first step in this direction. In this study, we demonstrate that we can detect the onset of intended movements and their direction using electrocorticographic (ECoG) signals recorded from the surface of the cortex in humans. We also demonstrate in a simulation that the information encoded in ECoG about these movements may improve performance in a targeting task. In summary, the results in this paper suggest that detection of intended movement is possible, and may serve useful functions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
Hungary 1 <1%
India 1 <1%
United Kingdom 1 <1%
Japan 1 <1%
Unknown 102 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 30%
Researcher 17 16%
Student > Master 14 13%
Student > Bachelor 9 8%
Student > Doctoral Student 6 6%
Other 19 18%
Unknown 11 10%
Readers by discipline Count As %
Engineering 33 31%
Agricultural and Biological Sciences 14 13%
Medicine and Dentistry 14 13%
Computer Science 12 11%
Neuroscience 11 10%
Other 7 6%
Unknown 17 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 June 2023.
All research outputs
#16,133,206
of 23,934,148 outputs
Outputs from Frontiers in Neuroengineering
#49
of 82 outputs
Outputs of similar age
#168,900
of 249,520 outputs
Outputs of similar age from Frontiers in Neuroengineering
#8
of 17 outputs
Altmetric has tracked 23,934,148 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 82 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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 249,520 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.