Dynamic discrimination analysis: A spatial-temporal SVM.
88 - 99.
Recently, pattern recognition methods (e.g., support vector machines (SVM)) have been used to analyze fMRI data. In these applications the fMRI scans are treated as spatial patterns and statistical learning methods are used to identify statistical properties of the data that discriminate between brain states (e.g., task 1 vs. task 2) or group of subjects (e.g., patients and controls). We propose an extension of these approaches using temporal embedding. This makes the dynamic aspect of fMRI time series an explicit part of the classification. The proposed pattern recognition approach uses both spatial and temporal information. Temporal embedding was implemented by defining spatiotemporal fMRI observations and applying a support vector machine to these temporally extended observations. This produces a discriminating weight vector encompassing both voxels and time. The resulting vector furnishes discriminating responses, at each voxel without imposing any constraints on their temporal form. (C) 2007 Elsevier Inc. All rights reserved.
|Title:||Dynamic discrimination analysis: A spatial-temporal SVM|
|Keywords:||machine learning methods, support vector machine, classifiers, functional magnetic resonance imaging data analysis, dynamic analysisx, SUPPORT VECTOR MACHINES, VISUAL-CORTEX, PATTERNS, STATES|
|UCL classification:||UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Imaging Neuroscience
UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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