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Meeting Number:   5

June 14, 2006


Exploratory Analysis of Brain Imaging Data


Dr. Tulay Adali
Department of Computer Science and Electrical Engineering
University of Maryland Baltimore County


Wednesday, June 14, 2006


5:45 PM:   Snacks
6:00 PM:   Talk begins


Historical Electronics Museum (HEM)
1745 W. Nursery Road, Linthicum, MD 21090

Please Respond To

Please respond to if you are planning to attend this meeting. Also, let me know if you will be joining us afterwards for dinner so I can make reservations. Dr. Adali suggested that we try G&M, located on N. Hammonds Ferry Rd., near the corner of Nursery Rd., in Linthicum. It’s maybe two miles down Nursury Rd. Again, only the speaker’s dinner is paid for. The rest of us need to pay our own way.


Independent component analysis (ICA) is a data analysis method used for discovering hidden factors, "sources", in 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.

A good example is our recent study of brain activity during simulated driving. In addition, we have demonstrated the advantages of ICA in the identification of various signal-types (e.g. task and transiently task-related, and physiology-related signals), and extended ICA to analysis of multi-subject and complex-valued fMRI data. In this talk, I will first introduce ICA and the basic motivation for using ICA on medical imaging data, and then will review the current work on ICA of medical imaging data with some specific examples from our own work.


Tulay Adali received the Ph.D. degree in electrical engineering from North Carolina State University, Raleigh, in 1992 and joined the faculty at the University of Maryland Baltimore County (UMBC), Baltimore, the same year. She is currently a professor in the Department of Computer Science and Electrical Engineering at UMBC. She worked in the organization of a number of international conferences and workshops including the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), the IEEE International Workshop on Neural Networks for Signal Processing (NNSP), and the IEEE International Workshops on Machine Learning for Signal Processing (MLSP). She was the general co-chair for the NNSP workshops 2001--2003 and the technical chair of the MLSP workshops 2004--2006.

She is the past chair and current member of the MLSP Technical Committee, and is serving on the IEEE publications board and the IEEE Signal Processing Society conference board.

Her research interests are in the areas of statistical signal processing, machine learning for signal processing, biomedical data analysis (functional MRI, MRI, PET, CR, ECG, and EEG), bioinformatics, and signal processing for optical communications. Dr. Adali is the recipient of a 1997 National Science Foundation CAREER Award.

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