Bioelectric Source Localization in Peripheral Nerves

Currently there does not exist a type of peripheral nerve interface that adequately combines spatial selectivity, spatial coverage and low invasiveness. In order to address this lack, we investigated the application of bioelectric source localization algorithms, adapted from electroencephalography/m...

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Main Author: Zariffa, Jose
Other Authors: Popovic, Milos R.
Language:en_ca
Published: 2009
Subjects:
Online Access:http://hdl.handle.net/1807/19115
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spelling ndltd-LACETR-oai-collectionscanada.gc.ca-OTU.1807-191152014-01-29T03:29:28ZBioelectric Source Localization in Peripheral NervesZariffa, JoseNeural interfacesSource localizationPeripheral nervesNerve cuff electrodesNeuroprostheses05410544Currently there does not exist a type of peripheral nerve interface that adequately combines spatial selectivity, spatial coverage and low invasiveness. In order to address this lack, we investigated the application of bioelectric source localization algorithms, adapted from electroencephalography/magnetoencephalography, to recordings from a 56-contact “matrix” nerve cuff electrode. If successful, this strategy would enable us to improve current neuroprostheses and conduct more detailed investigations of neural control systems. Using forward field similarities, we first developed a method to reduce the number of unnecessary variables in the inverse problem, and in doing so obtained an upper bound on the spatial resolution. Next, a simulation study of the peripheral nerve source localization problem revealed that the method is unlikely to work unless noise is very low and a very accurate model of the nerve is available. Under more realistic conditions, the method had localization errors in the 140 μm-180 μm range, high numbers of spurious pathways, and low resolution. On the other hand, the simulations also showed that imposing physiologically meaningful constraints on the solution can reduce the number of spurious pathways. Both the influence of the constraints and the importance of the model accuracy were validated experimentally using recordings from rat sciatic nerves. Unfortunately, neither idealized models nor models based on nerve sample cross-sections were sufficiently accurate to allow reliable identification of the branches stimulated during the experiments. To overcome this problem, an experimental leadfield was constructed using training data, thereby eliminating the dependence on anatomical models. This new strategy was successful in identifying single-branch cases, but not multi-branches ones. Lastly, an examination of the information contained in the matrix cuff recordings was performed in comparison to a single-ring configuration of contacts. The matrix cuff was able to achieve better fascicle discrimination due to its ability to select among the most informative locations around the nerve. These findings suggest that nerve cuff-based neuroprosthetic applications would benefit from implanting devices with a large number of contacts, then performing a contact selection procedure. Conditions that must be met before source localization approaches can be applied in practice to peripheral nerves were also discussed.Popovic, Milos R.2009-112010-02-23T21:00:48ZNO_RESTRICTION2010-02-23T21:00:48Z2010-02-23T21:00:48ZThesishttp://hdl.handle.net/1807/19115en_ca
collection NDLTD
language en_ca
sources NDLTD
topic Neural interfaces
Source localization
Peripheral nerves
Nerve cuff electrodes
Neuroprostheses
0541
0544
spellingShingle Neural interfaces
Source localization
Peripheral nerves
Nerve cuff electrodes
Neuroprostheses
0541
0544
Zariffa, Jose
Bioelectric Source Localization in Peripheral Nerves
description Currently there does not exist a type of peripheral nerve interface that adequately combines spatial selectivity, spatial coverage and low invasiveness. In order to address this lack, we investigated the application of bioelectric source localization algorithms, adapted from electroencephalography/magnetoencephalography, to recordings from a 56-contact “matrix” nerve cuff electrode. If successful, this strategy would enable us to improve current neuroprostheses and conduct more detailed investigations of neural control systems. Using forward field similarities, we first developed a method to reduce the number of unnecessary variables in the inverse problem, and in doing so obtained an upper bound on the spatial resolution. Next, a simulation study of the peripheral nerve source localization problem revealed that the method is unlikely to work unless noise is very low and a very accurate model of the nerve is available. Under more realistic conditions, the method had localization errors in the 140 μm-180 μm range, high numbers of spurious pathways, and low resolution. On the other hand, the simulations also showed that imposing physiologically meaningful constraints on the solution can reduce the number of spurious pathways. Both the influence of the constraints and the importance of the model accuracy were validated experimentally using recordings from rat sciatic nerves. Unfortunately, neither idealized models nor models based on nerve sample cross-sections were sufficiently accurate to allow reliable identification of the branches stimulated during the experiments. To overcome this problem, an experimental leadfield was constructed using training data, thereby eliminating the dependence on anatomical models. This new strategy was successful in identifying single-branch cases, but not multi-branches ones. Lastly, an examination of the information contained in the matrix cuff recordings was performed in comparison to a single-ring configuration of contacts. The matrix cuff was able to achieve better fascicle discrimination due to its ability to select among the most informative locations around the nerve. These findings suggest that nerve cuff-based neuroprosthetic applications would benefit from implanting devices with a large number of contacts, then performing a contact selection procedure. Conditions that must be met before source localization approaches can be applied in practice to peripheral nerves were also discussed.
author2 Popovic, Milos R.
author_facet Popovic, Milos R.
Zariffa, Jose
author Zariffa, Jose
author_sort Zariffa, Jose
title Bioelectric Source Localization in Peripheral Nerves
title_short Bioelectric Source Localization in Peripheral Nerves
title_full Bioelectric Source Localization in Peripheral Nerves
title_fullStr Bioelectric Source Localization in Peripheral Nerves
title_full_unstemmed Bioelectric Source Localization in Peripheral Nerves
title_sort bioelectric source localization in peripheral nerves
publishDate 2009
url http://hdl.handle.net/1807/19115
work_keys_str_mv AT zariffajose bioelectricsourcelocalizationinperipheralnerves
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