  Electroencephalography (EEG) serves as an effective diagnostic tool for
mental disorders and neurological abnormalities. Enhanced analysis and
classification of EEG signals can help improve detection performance. A new
approach is examined here for enhancing EEG classification performance by
leveraging knowledge of spatial layout of EEG sensors. Performance of two
classification models - model 1 that ignores the sensor layout and model 2 that
factors it in - is investigated and found to achieve consistently higher
detection accuracy. The analysis is based on the information content of these
signals represented in two different ways: concatenation of the channels of the
frequency bands and an image-like 2D representation of the EEG channel
locations. Performance of these models is examined on two tasks, social anxiety
disorder (SAD) detection, and emotion recognition using a dataset for emotion
analysis using physiological signals (DEAP). We hypothesized that model 2 will
significantly outperform model 1 and this was validated in our results as model
2 yielded $5$--$8\%$ higher accuracy in all machine learning algorithms
investigated. Convolutional Neural Networks (CNN) provided the best performance
far exceeding that of Support Vector Machine (SVM) and k-Nearest Neighbors
(kNNs) algorithms.
