Internship on Statistical Methods for Brain Signals
ApplyProject Description
The student will learn the state-of-the-art methods for pre-processing and modeling brain signals (in particular, electroencephalograms (EEG)). The student will explore various measures of brain functional and effective connectivity through numerical simulations and actual EEG recordings during various learning tasks. At the end of the internship, the student is expected to submit a written report, a poster and give an informal seminar in the Statistics Program.
Program -
Applied Mathematics and Computer Science
Division -
Computer, Electrical and Mathematical Sciences and Engineering
Field of Study -
Computer Science, Engineering, Statistics, Applied Mathematics, Economics, Physics.
About the
Researcher
Hernando Ombao
Professor, Statistics
Professor Ombao’s research interest is in the statistical
modeling of time series data and visualization of high dimensional signals and
images. He has developed a coherent set of methods for modeling
and inference on dependence in complex brain signals; testing for differences
in networks across patient groups; biomarker identification
and disease classification based on networks and in modeling association
between high dimensional data from different domains (e.g.,
genetics, brain function and behavior).
modeling of time series data and visualization of high dimensional signals and
images. He has developed a coherent set of methods for modeling
and inference on dependence in complex brain signals; testing for differences
in networks across patient groups; biomarker identification
and disease classification based on networks and in modeling association
between high dimensional data from different domains (e.g.,
genetics, brain function and behavior).
Desired Project Deliverables
At the end of the internship, the student is expected to submit a written report, a poster and give an informal seminar in the Statistics Program.