  The problem of detecting the presence of Social Anxiety Disorder (SAD) using
Electroencephalography (EEG) for classification has seen limited study and is
addressed with a new approach that seeks to exploit the knowledge of EEG sensor
spatial configuration. Two classification models, one which ignores the
configuration (model 1) and one that exploits it with different interpolation
methods (model 2), are studied. Performance of these two models is examined for
analyzing 34 EEG data channels each consisting of five frequency bands and
further decomposed with a filter bank. The data are collected from 64 subjects
consisting of healthy controls and patients with SAD. Validity of our
hypothesis that model 2 will significantly outperform model 1 is borne out in
the results, with accuracy $6$--$7\%$ higher for model 2 for each machine
learning algorithm we investigated. Convolutional Neural Networks (CNN) were
found to provide much better performance than SVM and kNNs.
