NEURO M206
Neuroengineering
Description: (Same as Bioengineering M260 and Electrical Engineering M255.) Lecture, four hours; laboratory, three hours; outside study, five hours. Requisites: Mathematics 32A, Physics 1B or 6B. Introduction to principles and technologies of bioelectricity and neural signal recording, processing, and stimulation. Topics include bioelectricity, electrophysiology (action potentials, local field potentials, EEG, ECOG), intracellular and extracellular recording, microelectrode technology, neural signal processing (neural signal frequency bands, filtering, spike detection, spike sorting, stimulation artifact removal), brain-computer interfaces, deep-brain stimulation, and prosthetics. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Fall 2022 - This class needs a rework - the course description does not match this class at all. I went in thinking we would learn about various technologies and applications of research in neural engineering, but every lecture is a convoluted presentation on deriving various circuit models of electrodes. The prerequisite is Physics 1B/5C, but the circuitry is more complicated. The homework isn't that bad, but you have to sort through a bunch of slides of nonsensical proofs and the occasional cool picture of an application to get the right formulas. The final project, which involves a fair bit of signal processing and machine learning, involves NONE of what the class teaches up to that point. Professor Liu is nice though, but wouldn't recommend this class.
Fall 2022 - This class needs a rework - the course description does not match this class at all. I went in thinking we would learn about various technologies and applications of research in neural engineering, but every lecture is a convoluted presentation on deriving various circuit models of electrodes. The prerequisite is Physics 1B/5C, but the circuitry is more complicated. The homework isn't that bad, but you have to sort through a bunch of slides of nonsensical proofs and the occasional cool picture of an application to get the right formulas. The final project, which involves a fair bit of signal processing and machine learning, involves NONE of what the class teaches up to that point. Professor Liu is nice though, but wouldn't recommend this class.
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