Explores neurobiological signal processing, covering spike modeling, signal classification, and data characterization using principal component analysis.
Introduces mathematical tools for communication systems and data science, focusing on stochastic processes and preparing students for advanced courses.
Covers the organization of the course, grading policy, bioelectricity, CMOS ICs, societal shifts, common medical measurands, and bioimplantable systems.
Explores linear prediction, optimal filters, random signals, stationarity, autocorrelation, power spectral density, and Fourier transform in signal processing.
