The University of Texas at Austin 3 credits Electrical and Computer Engineering
Examine the architecture of a brain-computer interface (BCI), the brain signals that can be recorded as input to a BCI, and the interaction paradigms where these signals can be decoded and exploited. Study the design principles of complex brain-controlled devices, with an emphasis on electroencephalogram (EEG) signals, the most common signal for BCI. Explore how to enhance EEG signal-to-noise ratio, and techniques to decode subjects' intents and cognitive states. Apply those techniques EEG signals acquired during closed-loop BCI experiments. Electrical and Computer Engineering 385J (Topic 35) and Electrical Engineering 385V (Topic: Brain Computer Interaction) may not both be counted. Pre/Corequisites: Course in Data Mining or Data Science Principles and course in Digital Signal Processing or equivalent; or consent of instructor.
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