Biosignals and Systems

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3 Credits

EECE 434

Data acquisition, time and frequency domain analysis, analog and discrete filter design, sampling theory, time-dependent processing, linear prediction, random signals, biomedical system modeling, and stability analysis; introduction to nonlinear systems.

Course Outline

In this course we will focus on the basic concepts, methodologies and tools of biosignal processing. This course introduces basic digital signal processing theory in the context of biomedical applications. Major topics of interest include: Data acquisition, time and frequency domain analysis, analog and discrete filter design, sampling theory, time- dependent processing, introduction to Wavelet, linear prediction, random signals, biomedical system modeling, and stability analysis; introduction to nonlinear systems.
All methods will be developed to address certain concerns on specific data sets in modalities such as EEG, speech signal, fMRI. The lectures will be accompanied by data analysis assignments using MATLAB. Students will explore the basics of biosignal processing and gain the hands-on experience with MATLAB® Signal Processing Toolbox by doing homework assignments and a term project.

Course Topics

  • Introduction and basics
  • Data acquisition (sampling and reconstruction of signals)
  • Discrete-time signals and systems
  • Discrete fourier transform (DFT)
  • Digital Filter Design
  • Multirate digital signal processing
  • Random variables and stochastic processes
  • Examples of biomedical signal processing
     

Prerequisites

All of
EECE 331 - Biomedical Engineering Instrumentation
EECE 359 - Signals and Communications
EECE 360 - Systems and Control
STAT 251 - Elementary Statistics
CPSC 260 - Object-Oriented Program Design
Professor: 

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