PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Unsupervised fMRI Analysis
David Hardoon, Janaina Mourao-Miranda, Michael Brammer and John Shawe-Taylor
In: New Directions on Decoding Mental States from fMRI Data, 08 Dec 2006, Whistler, Canada.

Abstract

Recently machine learning methodology has been used increasing to analyze the relationship between stimulus categories and fMRI responses. Here, we introduce a new unsupervised machine learning approach to fMRI analysis approach, in which the simple categorical description of stimulus type (e.g. type of task) is replaced by a more informative vector of stimulus features. We compared this new approach with a standard Support Vector Machine (SVM) analysis of fMRI data using a categorical description of stimulus type.

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EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:2757
Deposited By:David Hardoon
Deposited On:22 November 2006