Automated classification of fMRI data employing trial-based imagery tasks

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Automated interpretation and classification of functional MRI (fMRI) data is an emerging research field that enables the characterization of underlying cognitive processes with minimal human intervention. In this work, we present a method for the automated classification of human thoughts reflected on a trial-based paradigm using fMRI with a significantly shortened data acquisition time (less than one minute). Based on our preliminary experience with various cognitive imagery tasks, six characteristic thoughts were chosen as target tasks for the present work: right-hand motor imagery, left-hand motor imagery, right foot motor imagery, mental calculation, internal speech/word generation, and visual imagery. These six tasks were performed by five healthy volunteers and functional images were obtained using a T(2)*-weighted echo planar imaging (EPI) sequence. Feature vectors from activation maps, necessary for the classification of neural activity, were automatically extracted from the regions that were consistently and exclusively activated for a given task during the training process. Extracted feature vectors were classified using the support vector machine (SVM) algorithm. Parameter optimization, using a k-fold cross validation scheme, allowed the successful recognition of the six different categories of administered thought tasks with an accuracy of 74.5% (mean) +/- 14.3% (standard deviation) across all five subjects. Our proposed study for the automated classification of fMRI data may be utilized in further investigations to monitor/identify human thought processes and their potential link to hardware/computer control. (C) 2009 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2009
Language
English
Article Type
Article
Keywords

BRAIN-COMPUTER INTERFACES; SUPPORT VECTOR MACHINES; RESONANCE-IMAGING FMRI; CEREBRAL-BLOOD-FLOW; EVENT-RELATED FMRI; MOTOR IMAGERY; FUNCTIONAL MRI; REAL-TIME; MENTAL-IMAGERY; SENSORY STIMULATION

Citation

MEDICAL IMAGE ANALYSIS, v.13, no.3, pp.392 - 404

ISSN
1361-8415
DOI
10.1016/j.media.2009.01.001
URI
http://hdl.handle.net/10203/94061
Appears in Collection
RIMS Journal Papers
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