A SAMPLE PROJECT USING EEG SIGNALSCatalog Description: Prerequisites:
- Linear Systems and Introduction to Signal Processing or
equivalent with permission of instructor.
- MATLAB programming skills.
Scope:
Basics of bioelectrical signals (Electroencephalograms, Evoked potentials, Electromyograms, Electrocardiograms), their acquisition, modeling and analysis.
Signal Processing Methods:
Linear filtering, autocorrelation and covariance, Fourier-based spectral analysis, the short-time
Fourier transform, time-frequency analysis. Model-based spectral analysis.
Stochastic signals and noise reduction.
Signal representation in orthogonal bases: Karhunen-Loeve and wavelet transforms.
Adaptive filtering: Instantaneous and block LMS methods.
Text Book:
Bioelectrical Signal Processing in Cardiac and Neurological Applications by Leif Sornmo and Pablo Laguna. Elsevier Academic Press, 2005.
Projects and data:
www.biosignal.lth.se
http://www.physionet.org/
http://www.physionet.org/physiotools/ecgsyn/
Instructor:
Dr. Nurgun Erdol, Professor
Department of Electrical Engineering
E-mail: erdol@fau.edu
Telephone: 561-297-3409
Goals: This course is intended to provide a comprehensive overview of techniques of processing bioelectrical signals. It will be problem-based and programming oriented.
Students are expected to code in MATLAB at a level where they can use programming to verify and demonstrate concepts. Demonstration of work will be done with synthetically generated waveforms and real data.
Studio Time: T, Tr 9:30-10:50 am
Office Hours: T,Tr 2:00-3:30 pm and by email and blackboard. Please put BioSignals Processing in the subject field of your email.
Topics:
- Basics of Bioelectrical Signals – Chapter 1
- The Electrocardiogram Signal Processing – Chapters 6 & 7
- The Electroencephalogram (EEG) – Chapter 2
- EEG Signal Processing- Chapter 3
- Evoked Potentials – Chapter 4
- The Electromyogram -Chapter 5
Grading:
Grading is based on 4 projects and reports each of weight 25%.
Web access: Course materials, homework assignments and announcements will be posted on http://blackboard.fau.edu. Students may access the site by logging in according to the menu on the web site. You will need your SOCIAL SECURITY OR STUDENT NUMBER.