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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/19115

Title: Bioelectric Source Localization in Peripheral Nerves
Authors: Zariffa, Jose
Advisor: Popovic, Milos R.
Department: Electrical and Computer Engineering
Keywords: Neural interfaces
Source localization
Peripheral nerves
Nerve cuff electrodes
Neuroprostheses
Issue Date: 23-Feb-2010
Abstract: 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.
URI: http://hdl.handle.net/1807/19115
Appears in Collections:Doctoral
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering - Doctoral theses

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