Localization and Recovery of Peripheral Neural Sources With Beamforming Algorithms
Open accessAbstract
The peripheral nervous system carries sensory and motor information that could be useful as command signals for function restoration in areas such as neural prosthetics and functional electrical stimulation (FES). Nerve cuff electrodes provide a robust and safe technique for recording nerve signals. However, a method to separate and recover signals from individual fascicles is necessary. Prior knowledge of the electrode geometry was used to develop an algorithm which assumes neither signal independence nor detailed knowledge of the nerve's geometry/conductivity, and is applicable to any wide-band near-field situation. When used to recover fascicular activities from simulated nerve cuff recordings in a realistic human femoral nerve model, this beamforming algorithm separates signals as close as 1.5 mm with cross-correlation coefficient, R > 0.9 (10% noise). Ten simultaneous signals could be recovered from individual fascicles with only a 20% decrease in R compared to a single signal. At high noise levels (40%), sources were localized to 180 +/- 170 microm in the 12 x 3 mm cuff. Localizing sources and using the resulting positions in the recovery algorithm yielded R = 0.66 +/- 0.10 in 10% noise for five simultaneous muscle-activation signals from synergistic fascicles. These recovered signals should allow natural, robust, closed-loop control of multiple degree-of-freedom prosthetic devices and FES systems.
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Data from multiple electrodes near the nerve can be used to estimate the origin of the signal using signal processing techniques such as spatial filtering.
“In addition, the data from multiple electrodes near the nerve can be used to estimate the origin of the signal using various signal processing techniques, like spatial filtering (Wodlinger and Durand, 2009).”
From Update on Peripheral Nerve Electrodes for Closed-Loop Neuroprosthetics (2018) · cites this paper as Wodlinger and Durand, 2009
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A spiral nerve cuff electrode for peripheral nerve stimulation
Naples, G. G., Mortimer, J. T., Scheiner, A., & Sweeney, J. D. (1988) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.8670 · Cited by 415 · see citing works
Micera, S., Jensen, W., Sepulveda, F., Riso, R. R., & Sinkjaer, T. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.930903 · Cited by 58 · see citing works
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Research works printed but not linked: 18
- [1]Loeb GE Peck RA Cuff Electrodes For Chronic Stimulation And Recording Of Peripheral Nerve Activity J Neurosci Methods 64 95 103 1 1996 8869489
- [3]Rozman J Zorko B Bunc M Selective Recording Of Electroneurograms From The Sciatic Nerve Of A Dog With Multi-Electrode Spiral Cuffs Jpn J Physiol 50 509 14 10 2000 11120917
- [4]Tesfayesus W Yoo P Moffitt M Durand DM Blind Source Separation Of Nerve Cuff Recordings Proceedings Of The 25th International Conference Of The Ieee/Embs Cancun, Mexico 2003 17271612
- [5]Tesfayesus W Durand DM Blind Source Separation Of Peripheral Nerve Recordings J Neural Eng 4 S157 67 9 2007 17873415
- [8]Zariffa J Popovic MR Solution Space Reduction In The Peripheral Nerve Source Localization Problem Using Forward Field Similarities Journal Of Neural Engineering 5 191 202 6 2008 18460742
- [9]Zariffa J Popovic MR Application Of Eeg Source Localization Algorithms To The Monitoring Of Active Pathways In Peripheral Nerves 30th Annual International Ieee Embs Conference Vancouver, British Columbia, Canada 2008 19163642
- [10]Sekihara K Nagarajan SS Adaptive Spatial Filters For Electromagnetic Brain Imaging Springer 2008
- [12]Weinstein D Zhukov L Johnson C Lead-Field Bases For Electroencephalography Source Imaging Ann Biomed Eng 28 1059 65 9 2000 11132189
- [14]Otsu N A Threshold Selection Method From Gray-Level Histograms Ieee Transactions On Systems, Man, And Cybernetics 9 62 66 1979
- [15]Schiefer MA Triolo RJ Tyler DJ A Model Of Selective Activation Of The Femoral Nerve With A Flat Interface Nerve Electrode For A Lower Extremity Neuroprosthesis Ieee Trans Neural Syst Rehabil Eng 16 195 204 4 2008 18403289 PMC2920206
- [16]Moore JW Joyner RW Brill MH Waxman SD Najar-Joa M Simulations Of Conduction In Uniform Myelinated Fibers. Relative Sensitivity To Changes In Nodal And Internodal Parameters Biophys J 21 147 60 2 1978 623863 PMC1473353
- [17]Meeks JP Jiang X Mennerick S Action Potential Fidelity During Normal And Epileptiform Activity In Paired Soma-Axon Recordings From Rat Hippocampus J Physiol 566 425 41 7 15 2005 15890699 PMC1464751
- [18]Jezernik S Sinkjaer T On Statistical Properties Of Whole Nerve Cuff Recordings Ieee Trans Biomed Eng 46 1240 5 10 1999 10513129
- [19]Kenneth E Hild I Erdogmus Deniz Príncipe José Blind Source Separation Using Renyi’s Mutual Information Ieee Signal Processing Letters 8 2001
- [20]Pascual-Marqui RD Standardized Low-Resolution Brain Electromagnetic Tomography (Sloreta): Technical Details Methods Find Exp Clin Pharmacol 24 Suppl D 5 12 2002 12575463
- [21]Gorodnitsky IF Rao BD Sparse Signal Reconstruction From Limited Data Using Focuss: A Re-Weighted Minimum Norm Algorithm Ieee Transactions On Signal Processing 45 600 616 3 1997
- [22]Han S Jeong KH Principe J Robust Adaptive Minimum Entropy Beamformer In Impulsive Noise Ieee Workshop On Machine Learning For Signal Processing 2007 437 440
- [24]Choi AQ Cavanaugh JK Durand DM Selectivity Of Multiple-Contact Nerve Cuff Electrodes: A Simulation Analysis Ieee Trans Biomed Eng 48 165 72 2 2001 11296872
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- Authors
- Wodlinger, B., & Durand, D. M.
- Affiliations
- Case Western Reserve University (US)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2009-10
- Volume
- 17
- Issue
- 5
- Pages
- 461-468
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- PMC
- PMC3568387
- OpenAlex
- W2106364212
- Cited by
- 73
- References
- 2
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 2 · 2022: 6 · 2021: 2 · 2020: 8 · 2019: 6 · 2018: 6 · 2017: 3 · 2016: 9 · 2015: 2 · 2014: 7 · 2013: 1 · 2012: 4
- FWCI
- 2.49
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS032845)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- beamforming, cuff electrodes, peripheral nerve recording, functional electrical stimulation, neuroprosthetics, feedback control
- MeSH terms
- Action Potentials, Algorithms, Animals, Computer Simulation, Electrodes, Implanted, Equipment Design, Humans, Models, Neurological, Peripheral Nerves, Sensitivity and Specificity, Reproducibility of Results, Computer-Aided Design, Equipment Failure Analysis
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