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.
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- Electrical Engineering
- Biomedical Engineering
- Algorithm Development
- Bayesian methods
- Hardware Implementation
- Neural Activity Tracking
- Field programmable gate arrays
- Bayesian statistical decision theory
- Electroencephalography--Data processing.
- Signal processing--Digital techniques.
- Partial requirement for: Ph.D., Arizona State University, 2013Note typethesis
- Includes biliographical references (p. 95-103)Note typebibliography
- Field of study: Electrical engineering