Feature selection for fMRI-based deception detection.
BACKGROUND
Functional magnetic resonance imaging (fMRI) is a technology used to detect brain activity. Patterns of brain activation have been utilized as biomarkers for various neuropsychiatric applications. Detecting deception based on the pattern of brain activation characterized with fMRI is getting attention - with machine learning algorithms being applied to this field in recent years. The high dimensionality of fMRI data makes it a difficult task to directly utilize the original data as input for classification algorithms in detecting deception. In this paper, we investigated the procedures of feature selection to enhance fMRI-based deception detection.


RESULTS
We used the t-statistic map derived from the statistical parametric mapping analysis of fMRI signals to construct features that reflect brain activation patterns. We subsequently investigated various feature selection methods including an ensemble method to identify discriminative features to detect deception. Using 124 features selected from a set of 65,166 original features as inputs for a support vector machine classifier, our results indicate that feature selection significantly enhanced the classification accuracy of the support vector machine in comparison to the models trained using all features and dimension reduction based models. Furthermore, the selected features are shown to form anatomic clusters within brain regions, which supports the hypothesis that specific brain regions may play a role during deception processes.


CONCLUSIONS
Feature selection not only enhances classification accuracy in fMRI-based deception detection but also provides support for the biological hypothesis that brain activities in certain regions of the brain are important for discrimination of deception.
Jin
Bo
B
Medical University of South Carolina, Charleston, SC, USA. jinbo@musc.edu
Strasburger
Alvin
A
Laken
Steven J
SJ
Kozel
F Andrew
FA
Johnson
Kevin A
KA
George
Mark S
MS
Lu
Xinghua
X
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*