  In this paper, we have proposed a brain signal classification method, which
uses eigenvalues of the covariance matrix as features to classify images
(topomaps) created from the brain signals. The signals are recorded during the
answering of 2D and 3D questions. The system is used to classify the correct
and incorrect answers for both 2D and 3D questions. Using the classification
technique, the impacts of 2D and 3D multimedia educational contents on
learning, memory retention and recall will be compared. The subjects learn
similar 2D and 3D educational contents. Afterwards, subjects are asked 20
multiple-choice questions (MCQs) associated with the contents after thirty
minutes (Short-Term Memory) and two months (Long-Term Memory). Eigenvalues
features extracted from topomaps images are given to K-Nearest Neighbor (KNN)
and Support Vector Machine (SVM) classifiers, in order to identify the states
of the brain related to incorrect and correct answers. Excellent accuracies
obtained by both classifiers and by applying statistical analysis on the
results, no significant difference is indicated between 2D and 3D multimedia
educational contents on learning, memory retention and recall in both STM and
LTM.
