Neural and Physiological Signal Processing

(3 Credits)

This advanced-topic course will present signal processing and statistical methods used to study neural systems and analyze pulsatile physiological signals. Core topics include state-space modeling, state-space estimation, Kalman filtering, theory of point processes, estimation of point processes, maximum likelihood estimation, expectation maximization, point process filtering and smoothing, and sparse signal processing. Emphasis on developing a firm conceptual understanding of advanced signal processing and statistical methods primarily through analysis of experimental data in form of a course project. Applications of these theoretical techniques include dynamic analyses of neural encoding, neural spike train decoding, and pulsatile physiological data analysis. This is an advanced graduate class and knowledge of Probability theory, Signal processing, and MATLAB are prerequisites. | Prerequisites: Graduate Students, advisor's approval