Thesis
Signal separation algorithm for combinatoric multi-electrode probe design enabling increased neural recording yield
Master of Science (M.S.), Drexel University
Mar 2017
DOI:
https://doi.org/10.17918/00002065
Abstract
Neural signal recordings relay important information about the neuronal networks in the Central Nervous System (CNS), which allow the physician or an investigator to gain an understanding about the system's connections. Current computational approaches usually limit recordings to a single site on a wire. We seek instead, to exploit innovative work in the design of combinatoric multi-site electrode probes (Giszter et al.) using multisite wires. In this project, we utilize independent component analysis with reference (ICA-r) incorporated into a MATLAB algorithm to efficiently separate neural recording yields from each wire in the probe in a combinatoric design. First, we present a ₃C₂ combinatoric design with and without additive noise, with multiple neurons per recording-site. Additionally, we look at the signal-to-noise ratio of the noise carrying data, which is seen to vary depending on the type of noise introduced. Finally, we present an expanded combinatorics algorithm in a ₉C₄ design, once again with and without additive noise. For simple and expanded, pure signal scenarios, as well as in scenarios with added white noise, our algorithm allows for average achieval of 94% of original signal reconstruction. Additionally, we are able to identify the component neurons at each of the site, with 75% accuracy in the original ₃C₂ scenario. We have identified our opportunity statement as a chance to develop a novel signal processing tool to fully leverage the new multisite wire recording probe. Our project was driven by the claim that expanding the complexity of the design and test data will allow us to increase neural recording yield by at least an order of magnitude, while maintaining the precision of combinatoric multi-electrode probe signal separation.
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Details
- Title
- Signal separation algorithm for combinatoric multi-electrode probe design enabling increased neural recording yield
- Creators
- Joanna Wycech
- Contributors
- Simon F. Giszter (Advisor) - Drexel University, Drexel University (1970-)
- Awarding Institution
- Drexel University
- Degree Awarded
- Master of Science (M.S.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- vi, 39 pages
- Resource Type
- Thesis
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems (1997-2026); Drexel University
- Other Identifier
- 991021889057304721