Ni, Yizhao, Chu, Carlton, Saunders, Craig and Ashburner, John
Kernel methods for fmri pattern prediction
At WCCI 2008 (IJCNN 2008), China.
01 - 06 Jun 2008.
- Version of Record
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.
Conference or Workshop Item
||Event Dates: June 1-6, 2008
|Venue - Dates:
||WCCI 2008 (IJCNN 2008), China, 2008-06-01 - 2008-06-06
||Electronics & Computer Science
||30 Apr 2010 11:23
||17 Apr 2017 18:26
|Further Information:||Google Scholar|
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