Cognitive load level classification of EEG signals using SVM for intelligent tutoring systems - Regonia, Paul Rossener R.
Students experiencing information overload can benefit from an intelligent tutoring system that monitors their brain activities, and provides feedback based on their mental state. In this study, we recorded electroencephalogram (EEG) data of students as they performed mental task experiments. SVM classifiers were trained from the EEG data, and calibrated using band power spectral density (PSD) and event-related desynchronization (ERD) features extraction, feature selection, and grid seach parameter optimization. Using this procedure, we were able to achieve mean classification of 0.91 F-score for mental arithmetic task, and 0.92 F-score for mental word problem task. These classifiers can be used on a real-time intelligent tutoring system to detect student experiencing information overload by monitoring their cognitive load levels while they are studying.