↓ Skip to main content

Evoked Electromyographically Controlled Electrical Stimulation

Overview of attention for article published in Frontiers in Neuroscience, July 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 X users

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
49 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Evoked Electromyographically Controlled Electrical Stimulation
Published in
Frontiers in Neuroscience, July 2016
DOI 10.3389/fnins.2016.00335
Pubmed ID
Authors

Mitsuhiro Hayashibe

Abstract

Time-variant muscle responses under electrical stimulation (ES) are often problematic for all the applications of neuroprosthetic muscle control. This situation limits the range of ES usage in relevant areas, mainly due to muscle fatigue and also to changes in stimulation electrode contact conditions, especially in transcutaneous ES. Surface electrodes are still the most widely used in noninvasive applications. Electrical field variations caused by changes in the stimulation contact condition markedly affect the resulting total muscle activation levels. Fatigue phenomena under functional electrical stimulation (FES) are also well known source of time-varying characteristics coming from muscle response under ES. Therefore, it is essential to monitor the actual muscle state and assess the expected muscle response by ES so as to improve the current ES system in favor of adaptive muscle-response-aware FES control. To deal with this issue, we have been studying a novel control technique using evoked electromyography (eEMG) signals to compensate for these muscle time-variances under ES for stable neuroprosthetic muscle control. In this perspective article, I overview the background of this topic and highlight important points to be aware of when using ES to induce the desired muscle activation regardless of the time-variance. I also demonstrate how to deal with the common critical problem of ES to move toward robust neuroprosthetic muscle control with the Evoked Electromyographically Controlled Electrical Stimulation paradigm.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 48 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 31%
Student > Bachelor 7 14%
Researcher 5 10%
Student > Master 5 10%
Student > Doctoral Student 2 4%
Other 7 14%
Unknown 8 16%
Readers by discipline Count As %
Engineering 18 37%
Nursing and Health Professions 4 8%
Medicine and Dentistry 4 8%
Neuroscience 3 6%
Computer Science 2 4%
Other 7 14%
Unknown 11 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 September 2016.
All research outputs
#15,983,535
of 25,374,647 outputs
Outputs from Frontiers in Neuroscience
#6,982
of 11,542 outputs
Outputs of similar age
#219,259
of 371,041 outputs
Outputs of similar age from Frontiers in Neuroscience
#107
of 161 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 38th percentile – i.e., 38% 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 371,041 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 161 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.