Feature selection for fMRI-based deception detection

Abstract

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.

Conclusion: 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.

Figure 1

The number of the identified common features. The figure plots the number of the identified common features against the number of sets sharing them.

Figure 2

The classification performance using the selected features. (A) Classification accuracy. (B) Sensitivity. (C) Specificity. (D) PPV. The leave-one-out procedure was employed to evaluate the performance of SVM in deception detection using the selected features. The performance was measured in terms of accuracy, sensitivity, specificity and PPV.

Figure 3

The anatomic locations of the selection features. (A) The 124 selected features from the ensemble method were mapped back to the brain volume. (B) Overlay of the selected features to the regions identified by group-wise statistical analysis. The 124 selected features from the ensemble method were mapped to the brain regions identified by group-wise statistical test. In the figure, the 124 selected features are marked with the red color and the regions identified by group-wise statistical test are marked with the blue color.

Figure 4

Procedure of feature selection with the filter and wrapper methods. In the figure, GAR2W2 and GAJH follow the procedure marked with the blue color; FCS and Relief-F flow the dark-red-colored path.