Situational interest is widely explored in the psychology and education domains. It is proven to have positive effect on learning and academic achievement. Nonetheless, not much attention is given for assessing the feasibility of detecting this interest in natural classroom physiologically. Therefore, this study investigates the possibility of detecting situational interest using Electroencephalogram (EEG) in classroom. After preprocessing of EEG data, they were decomposed using Empirical Mode Decomposition (EMD). The resulted Intrinsic Mode Functions (IMFs) were ranked based on their significance using T-test and Receiver Operator Characteristics (ROC) in descending order. A matrix was constructed for all participants using the best six features from four EEG channels. These selected features were fed into Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers with 10 cross validation. While SVM achieved high accuracy of 93.3% and 87.5% for two data sets using features from the four EEG channels, KNN classifier achieved high accuracy of 87.5% and 86.7% in the same datasets using single EEG channel. It is found that gamma and delta bands can be used successfully to detect situational interest of students during learning in classrooms. Furthermore, data of single EEG channel - F3 in this study- was efficient to detect student’s situational interest in simultaneous recording of EEG in classroom.
ASJC Scopus subject areas
- Media Technology
- Hardware and Architecture
- Computer Networks and Communications