Rutgers University 3 credits Data Science
This course introduces different measures of brain activity, processing them, decoding stimuli or behaviors from them, and evaluating decoder performance. Students are introduced to the neurophysiology and processing of signals, EEG (scalp electrical activity), ECoG (brain surface electrical activity), and spiking (firing of neurons). With these signals, they are shown how to use a variety of logistic regression and Naïve Bayes classifiers to infer the presentation of stimuli or motor actions. Multiple publicly available data sets will be used, and all algorithms will be coded in Python. A few workshops will be held to reinforce previous lectures and discuss practical programming challenges. By the end of the course, students should appreciate issues in the design of machine learning systems and be able to implement their own decoders of brain activity.
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