Course Schedule and Catalog

Department: Electrical & Computer Eng.

Meeting: 4:00PM - 5:15PM TR (24-AUG-2026 - 4-DEC-2026) 

Part of Term: Full Term

Grade Mode: Standard Letter

Course Type: Lecture

Language of Instruction: Taught in English

Method of Instruction: Face to Face

Credit Hours: 3

 

Section Max Enrollment: 20

Section Enrolled: 19

Enrollment data as of: 26-APR-2026 9:10AM

 

 

Final Exam: Scheduled Final Exam-OTR Room

 

Description: Biomedical signals representing physiological processes help advance scientific understanding of the human body as well as enable translational/therapeutic applications. This course will introduce signal processing and machine learning methods for analyzing biomedical time series. First, students will gain an understanding of how common biological signals such as neural activity and muscle activity are generated and the typical issues with recording them (signal dispersion, recording instabilities, data scarcity, individual variability, etc). Second, they will learn commonly used statistical methods used to extract useful information from these biomedical signals. Methods will span spectral analysis, unsupervised discovery & latent variable models to identify structure in data and supervised methods to decode task-relevant information. While we focus on neurophysiological signal processing applications, the concepts are broadly applicable. Recommended Prerequisite(s): ELEC 303 Random Signals, probability or equivalent, ELEC 378 Fundamentals of Machine Learning, or equivalent, and ELEC 242 Signals, Systems and Transforms