Optimization of return electrodes in neurostimulating arrays
Journal of Neural Engineering, ISSN: 1741-2552, Vol: 13, Issue: 3, Page: 036010
2016
- 37Citations
- 65Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations37
- Citation Indexes37
- 37
- CrossRef18
- Captures65
- Readers65
- 65
Article Description
Objective. High resolution visual prostheses require dense stimulating arrays with localized inputs of individual electrodes. We study the electric field produced by multielectrode arrays in electrolyte to determine an optimal configuration of return electrodes and activation sequence. Approach. To determine the boundary conditions for computation of the electric field in electrolyte, we assessed current dynamics using an equivalent circuit of a multielectrode array with interleaved return electrodes. The electric field modeled with two different boundary conditions derived from the equivalent circuit was then compared to measurements of electric potential in electrolyte. To assess the effect of return electrode configuration on retinal stimulation, we transformed the computed electric fields into retinal response using a model of neural network-mediated stimulation. Main results. Electric currents at the capacitive electrode-electrolyte interface redistribute over time, so that boundary conditions transition from equipotential surfaces at the beginning of the pulse to uniform current density in steady state. Experimental measurements confirmed that, in steady state, the boundary condition corresponds to a uniform current density on electrode surfaces. Arrays with local return electrodes exhibit improved field confinement and can elicit stronger network-mediated retinal response compared to those with a common remote return. Connecting local return electrodes enhances the field penetration depth and allows reducing the return electrode area. Sequential activation of the pixels in large monopolar arrays reduces electrical cross-talk and improves the contrast in pattern stimulation. Significance. Accurate modeling of multielectrode arrays helps optimize the electrode configuration to maximize the spatial resolution, contrast and dynamic range of retinal prostheses.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84970027554&origin=inward; http://dx.doi.org/10.1088/1741-2560/13/3/036010; http://www.ncbi.nlm.nih.gov/pubmed/27098048; https://iopscience.iop.org/article/10.1088/1741-2560/13/3/036010; https://dx.doi.org/10.1088/1741-2560/13/3/036010; https://validate.perfdrive.com/fb803c746e9148689b3984a31fccd902/?ssa=d2290a90-f342-458e-96cc-b8f51dd89ed2&ssb=51856219355&ssc=https%3A%2F%2Fiopscience.iop.org%2Farticle%2F10.1088%2F1741-2560%2F13%2F3%2F036010&ssi=884df622-8427-42fc-b92b-d941cd08ac6b&ssk=support@shieldsquare.com&ssm=00264285222577394207979405586519440&ssn=2bdc5e264f88cb6010338e4449b209358e2efd3411ea-b241-4a9e-aec748&sso=ae63bc58-cab8b6d05de2d5fa769ee7facde4287457967e726238a656&ssp=99264900741716491018171678392298116&ssq=83476242398932989962500488816149530781461&ssr=NTIuMy4yMTcuMjU0&sst=com.plumanalytics&ssu=&ssv=&ssw=&ssx=eyJyZCI6ImlvcC5vcmciLCJfX3V6bWYiOiI3ZjYwMDBjZGFmYTA2NC1lZDRlLTRiMjUtOTBjZC0yOTRiYjhjNWY5ZjIxNzE2NDAwNDg4Nzk2MzIzNTAwNTYxLWYwZmY5OGMwZjlmY2YzZWQyMDc5NyIsInV6bXgiOiI3ZjkwMDBiYWVhY2MxNS03NDkxLTRhNjgtYTcxZi0wMDI2OTkxOTEyOGY1LTE3MTY0MDA0ODg3OTYzMjM1MDA1NjEtOTAyNWZiZjk2MDVmZDNlMzIwNzk3In0=
IOP Publishing
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know