Selective Prediction of Student Emotions based on Unusually Strong EEG Signals

Abstract

With an electroencephalogram (EEG) sensor mounted on their head while learning mathematics using two computer-based learning software, EEG signals were collected from fifty six (56) academically-gifted students of ages 11 to 14. The EEG signals are used to predict four academic emotions, namely frustrated, confused, bored, and interested. It is shown that emotion classification accuracy is improved by selective prediction - performed only when a pre-determined proportion of EEG feature values deviate significantly from the baseline mean. The experiments on instances, where 0%, 2%, 4%, and up to 20% of the features are signifi- cantly stronger EEG signals, show that the accuracy rate of decision trees increases from 0.50, 0.59, and 0.45 (for instances with 0% special event features) to 0.74, 0.75, and 0.66 (for in- stances with 20% special event features) for predicting frustrated, confused and bored, respec- tively. Accuracy for predicting interested does not increase like for the other three emotions.