Name
Title | ||
|---|---|---|
Adaptive parameter selection for asynchronous intrafascicular multi-electrode stimulation |
Abstract | ||
|---|---|---|
This paper describes an adaptive algorithm for selecting perelectrode stimulus intensities and inter-electrode stimulation phasing to achieve desired isometric plantar-flexion forces via asynchronous, intrafascicular multi-electrode stimulation. The algorithm employed a linear model of force production and a gradient descent approach for updating the parameters of the model. The adaptively selected model stimulation parameters were validated in experiments in which stimulation was delivered via a Utah Slanted Electrode Array that was acutely implanted in the sciatic nerve of an anesthetized feline. In simulations and experiments, desired steps in force were evoked, and exhibited short time-to-peak (<; 0.5 s), low overshoot (<; 10%), low steady-state error (<; 4%), and low steady-state ripple (<; 12%), with rapid convergence of stimulation parameters. For periodic desired forces, the algorithm was able to quickly converge and experimental trials showed low amplitude error (mean error <; 10% of maximum force), and short time delay (<; 250 ms). |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICASSP.2012.6287993 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
bioelectric phenomena,biomedical electrodes,gradient methods,medical computing,neuromuscular stimulation,parameter estimation,Utah slanted electrode array,adaptive algorithm,adaptive parameter selection,adaptively selected model stimulation parameters,asynchronous intrafascicular multielectrode stimulation,force production linear model,gradient descent approach,interelectrode stimulation phasing,isometric plantar-flexion forces,perelectrode stimulus intensity selecting,sciatic nerve,Animal Models,Functional Electrical Stimulation,Gradient Descent,Neuroprosthesis | Functional electrical stimulation,Gradient descent,Electrode array,Pattern recognition,Control theory,Computer science,Overshoot (signal),Artificial intelligence,Estimation theory,Adaptive algorithm,Steady state,Ripple | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4673-0044-5 | 978-1-4673-0044-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mitchell A. Frankel | 1 | 0 | 0.34 |
| Gregory A. Clark | 2 | 45 | 8.22 |
| Sanford G. Meek | 3 | 58 | 8.49 |
| Richard A. Normann | 4 | 43 | 9.07 |
| V. John Mathews | 5 | 38 | 11.28 |