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Seminar - Independent Component Analysis of fMRI Data: Recent Results and Future Directions
Date: May 7, 2004
Time: 3:00 PM
Location: Matheson Hall, Room: 208

Tulay Adali, PhD
Associate Professor
University of Maryland, Baltimore County

Independent component analysis (ICA) is a data analysis method used for discovering hidden factors (sources), given sets of signals (mixtures). ICA has found a fruitful application in the analysis of functional magnetic resonance imaging (fMRI) data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed a priori models of brain activity are not available.

ICA has been successfully utilized in a number of exciting fMRI applications for identification of various signal-types (e.g., task and transiently task-related, and physiology-related signals). In this talk, Dr. Adali will present a review of ICA of fMRI data and will give specific examples from our own work, which includes study of brain activity during simulated driving, extension of ICA to analyze fMRI data from multiple subjects, and processing of complex-valued data using analytic non-linearities. She will conclude with a discussion on future directions of research in the area.

Tulay Adali received the B.S. degree from Middle East Technical University, Ankara, Turkey, in 1987 and the M.S. and Ph.D. degrees from North Carolina State University, Raleigh, in 1988 and 1992 respectively, all in electrical engineering. In 1992, she joined the Department of Electrical Engineering at the University of Maryland Baltimore County, Baltimore, where she currently is an associate professor.She has worked in the organization of a number of international conference and workshops including the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) and the IEEE International Workshop on Neural Networks for Signal Processing (NNSP). She was the general co-chair for the NNSP workshops 2001-2003.

Dr. Adali is currently the chair for the IEEE Machine Learning for Signal Processing Technical Committee and is serving on the IEEE Signal Processing Society conference board. She is an associate editor for the IEEE Transactions on Signal Processing and the Journal of VLSI Signal Processing Systems. She has also guest-edited a number of special issues for the IEEE Transactions on Neural Networks and the VLSI Signal Processing Systems on biomedical, multimedia, and data mining applications of neural networks. Her research interests are in the areas of statistical signal processing, neural computation, adaptive signal processing, biomedical data analysis, bioinformatics, and communications. Dr. Adali is the recipient of a 1997 National Science Foundation CAREER Award and the provost's research faculty fellowship.

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