Kernel methods for fmri pattern prediction

Ni, Yizhao, Chu, Carlton , Saunders, Craig and Ashburner, John (2008) Kernel methods for fmri pattern prediction. At WCCI 2008 (IJCNN 2008), Hong Kong, China, 01 - 06 Jun 2008.


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In this paper, we present an effective computational approach for learning patterns of brain activity from the fMRI data. The procedure involved correcting motion artifacts, spatial smoothing, removing low frequency drifts and applying multivariate linear and non-linear kernel methods. Two novel techniques are applied: one utilizes the Cosine Transform to remove low-frequency drifts over time and the other involves using prior knowledge about the spatial contribution of different brain regions for the various tasks. Our experiment results on the PBAIC2007 competition data set show a great improvement for brain activity prediction, especially on some sensory experience such as hearing and vision.

Item Type: Conference or Workshop Item (Speech)
Additional Information: Event Dates: June 1-6, 2008
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science
ePrint ID: 270945
Accepted Date and Publication Date:
Date Deposited: 30 Apr 2010 11:23
Last Modified: 31 Mar 2016 14:17
Further Information:Google Scholar

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