Multiple studies show that electroencephalogram (EEG) signals behave differently when humans experience various emotions. The objective of this project is to create a model of human academic emotions (namely: boredom, confusion, engagement and frustration) using EEG signals. Raw EEG signals were collected from nineteen (19) students while solving Berg's Card Sorting Task. Noise reduction was performed using 8Hz-30Hz 10th-Order Butter worth Band pass Filter. The following statistical features of raw EEG signals were computed: mean, standard deviation, mean of absolute first and second differences and standardized mean of absolute first and second differences. The k-Nearest Neighbor, Support Vector Machines, and Multilayer Perceptron were used as classifiers. Accuracy scores (at their highest) were 54.09%, 46.86% and 40.72% respectively, using batch cross-validation.