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Analysis of fractal electrodes for efficient neural stimulation

Overview of attention for article published in Frontiers in Neuroengineering, January 2013
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Title
Analysis of fractal electrodes for efficient neural stimulation
Published in
Frontiers in Neuroengineering, January 2013
DOI 10.3389/fneng.2013.00003
Pubmed ID
Authors

Laleh Golestanirad, Behzad Elahi, Alberto Molina, Juan R. Mosig, Claudio Pollo, Robert Chen, Simon J. Graham

Abstract

Planar electrodes are increasingly used in therapeutic neural stimulation techniques such as functional electrical stimulation, epidural spinal cord stimulation (ESCS), and cortical stimulation. Recently, optimized electrode geometries have been shown to increase the efficiency of neural stimulation by increasing the variation of current density on the electrode surface. In the present work, a new family of modified fractal electrode geometries is developed to enhance the efficiency of neural stimulation. It is shown that a promising approach in increasing the neural activation function is to increase the "edginess" of the electrode surface, a concept that is explained and quantified by fractal mathematics. Rigorous finite element simulations were performed to compute electric potential produced by proposed modified fractal geometries. The activation of 256 model axons positioned around the electrodes was then quantified, showing that modified fractal geometries required a 22% less input power while maintaining the same level of neural activation. Preliminary in vivo experiments investigating muscle evoked potentials due to median nerve stimulation showed encouraging results, supporting the feasibility of increasing neural stimulation efficiency using modified fractal geometries.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Iran, Islamic Republic of 1 2%
Unknown 52 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 29%
Researcher 11 20%
Student > Bachelor 6 11%
Other 4 7%
Professor > Associate Professor 3 5%
Other 9 16%
Unknown 6 11%
Readers by discipline Count As %
Engineering 13 24%
Neuroscience 8 15%
Medicine and Dentistry 7 13%
Agricultural and Biological Sciences 4 7%
Physics and Astronomy 4 7%
Other 8 15%
Unknown 11 20%
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 24 July 2013.
All research outputs
#20,196,270
of 22,714,025 outputs
Outputs from Frontiers in Neuroengineering
#70
of 82 outputs
Outputs of similar age
#248,768
of 280,752 outputs
Outputs of similar age from Frontiers in Neuroengineering
#7
of 9 outputs
Altmetric has tracked 22,714,025 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% 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 7.2. This one is in the 1st percentile – i.e., 1% 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 280,752 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.