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Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation

Abstract Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems. First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estim... (more)
Created Date 2013
Contributor Miao, Lifeng (Author) / Chakrabarti, Chaitali (Advisor) / Papandreou-Suppappola, Antonia (Advisor) / Zhang, Junshan (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Biomedical engineering / Algorithm Development / Bayesian Methods / Hardware Implementation / Neural Activity Tracking
Type Doctoral Dissertation
Extent 116 pages
Language English
Reuse Permissions All Rights Reserved
Note Ph.D. Electrical Engineering 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis