Public profile
Research areas
intracortical brain computer Interfaces (BCIs), electrical stimulation, closed loop BCIs, computational neuroscience, neural signal processing
Electrical and Computer Engineering
Assistant Professor of Electrical and Computer Engineering
McNair Scholar in Neuroengineering
Member, Ken Kennedy Institute
Adjunct Assistant Professor of Neurosurgery at Baylor College of Medicine
Average rating
3.4
8 temporary mock ratings
Difficulty
2.5
course-linked average
Courses
4
in seeded sections
intracortical brain computer Interfaces (BCIs), electrical stimulation, closed loop BCIs, computational neuroscience, neural signal processing
APPL 800
Thesis research under the supervision of faculty. Repeatable for Credit.
BIOE 500
Repeatable for Credit.
ELEC 384
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
ELEC 800
Repeatable for Credit.