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Department of Statistical and Actuarial Sciences
Center of Actuarial Excellence (CAE)
Graduate

Analysis of brain imaging data

Statistical Science 9833B


Description: This interdisciplinary course provides a hands-on introduction into modern statistical approaches to the analysis of brain imaging signals, as used for neuroscience research and the development of brain-machine interfaces.

Term: B

Prerequisite(s): The course is targeted at MSc and PhD students in Statistics, Computer Science, Neuroscience, Psychology and Medical Biophysics. Basic programming skills in a high-level language such as Matlab, R, or Python are required for all students. For students with a neuroscience / psychology / medical biophysics background, basic experience with a brain imaging method, as well as basic knowledge in linear algebra and statistics (multiple regression) is required. Ideally neuroscience students would bring a data set from his/her own research for the project. For students with a computer science / statistics background prerequisites are a good grasp of general linear model analysis and an interest in complex data analysis.