Sparse Representation-based fMRI Decoding and Audio - visual Integration
Date: Friday 24 January 2014
Time: 1.30pm to 3.00pm
Location: CC05.GD.02
Seminar Chairman: Assoc. Professor Jinyan Li, Advanced Analytics Institute (AAi) – Jinyan.li@uts.edu.au
Abstract
Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we first introduced an iterative sparse representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. Next, we extended this algorithm such that (i) all the informative features for discrimination can be selected; (ii) these selected features can be separated into two sets corresponding to two stimulus classes/brain states; (iii) irrelevant/noisy features in the two selected feature sets can be removed. Data analysis results demonstrated the effectiveness of our algorithms. Third, using the MVPA method, we investigated cross modal integration in audio-visual facial perception and explored its effect on the neural representation of conceptual features. In our experiment, the subjects needed to make a judgment about the gender/emotion category for each facial stimulus in the audio-visual, visual-only, or auditory-only stimulus condition as fMRI signals were recorded. The neural representation of the gender/emotion feature was assessed using the decoding accuracy and the brain pattern-related reproducibility indices, obtained by a multivariate pattern analysis method from the fMRI data. In comparison to the visual-only and auditory-only stimulus conditions, we found that audio-visual integration enhanced the neural representation of task-relevant features and that feature-selective attention might play a role of modulation in the audio-visual integration.
Short biography of the speaker
Yuanqing Li received the B.S. degree in applied mathematics from Wuhan University, Wuhan, China, in 1988, the M.S. degree in applied mathematics from South China Normal University, Guangzhou, China, in 1994, and the Ph.D. degree in control theory and applications from South China University of Technology, Guangzhou, China, in 1997. Since 1997, he has been with the South China University of Technology, where he became a Full Professor in 2004. From 2002 to 2004, he was a Researcher at the Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Saitama, Japan. From 2004 to 2008, he was a Research Scientist at the Laboratory for Neural Signal Processing, Institute for Inforcomm Research, Singapore. He is the author or co-author of more than 80 scientific papers in journals and conference proceedings. His research interests include blind signal processing, sparse representation, machine learning, brain computer interfaces, EEG, and fMRI data analysis. Currently, he is the director of the Research Centre for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, the dean of the School of Automation Science and Engineering, South China University of Technology, and the AE of IEEE Trans. Fuzzy Systems.
Overview to AAI seminar series
The Advanced Analytics Seminar Series presents the latest theoretical advancement and empirical experience in a broad range of interdisciplinary and business-oriented analytics fields. It covers topics related to data mining, machine learning, statistics, bioinformatics, behavior informatics, marketing analytics and multimedia analytics. It also provides a platform for the showcase of commercial products in ubiquitous advanced analytics. Speakers are invited from both academia and industry. It opens regularly on selected Friday afternoons at the garden-like UTS Blackfriars Campus. You are warmly welcome to attend this seminar series.
Jinyan Li, Seminar Coordinator, Associate Professor
Advanced Analytics Institute, School of Software, Faculty of Engineering and IT
University of Technology, Sydney
P.O. Box 123, Broadway, NSW 2007, Australia
Tel: 02 95149264 (office)