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In this paper, we have proposed a brain signal classification method, whichuses eigenvalues of the covariance matrix as features to classify images(topomaps) created from the brain signals. The signals are recorded during theanswering of 2D and 3D questions. The system is used to classify the correctand incorrect answers for both 2D and 3D questions. Using the classificationtechnique, the impacts of 2D and 3D multimedia educational contents onlearning, memory retention and recall will be compared. The subjects learnsimilar 2D and 3D educational contents. Afterwards, subjects are asked 20multiple-choice questions (MCQs) associated with the contents after thirtyminutes (Short-Term Memory) and two months (Long-Term Memory). Eigenvaluesfeatures extracted from topomaps images are given to K-Nearest Neighbor (KNN)and Support Vector Machine (SVM) classifiers, in order to identify the statesof the brain related to incorrect and correct answers. Excellent accuraciesobtained by both classifiers and by applying statistical analysis on theresults, no significant difference is indicated between 2D and 3D multimediaeducational contents on learning, memory retention and recall in both STM andLTM.

Eigen Values Features for the Classification of Brain Signals corresponding to 2D and 3D Educational Contents