Description: Lecture, three hours. Recommended preparation: one term of graduate level statistics, biostatistics. Understanding of neural origins of electroencephalography (EEG), common and advanced methods for experiment designs, EEG recording and noise reduction, data processing, feature extractions, and biomarker development. Students design simple experimental paradigms to answer some fundamental perceptual and cognitive questions, de-noise already recorded EEG and extract useful information using popular EEG processing interfaces such as EEGLAB and BrainStorm, perform some common statistical tests on the extracted featured and explain achieved results, and navigate through state-of-the-art analyses and applications of EEG. Letter grading.

Units: 4.0