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.
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.
Prerequisites