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2011 Third International Conference on Knowledge and Systems Engineering
Measuring Academic Affective States of Students via Brainwave Signals
Hanoi, Vietnam
October 14-October 17
ISBN: 978-0-7695-4567-7
Multiple studies show that electroencephalogram (EEG) signals behave differently when humans experience various emotions. The objective of this project is to create a model of human academic emotions (namely: boredom, confusion, engagement and frustration) using EEG signals. Raw EEG signals were collected from nineteen (19) students while solving Berg's Card Sorting Task. Noise reduction was performed using 8Hz-30Hz 10th-Order Butter worth Band pass Filter. The following statistical features of raw EEG signals were computed: mean, standard deviation, mean of absolute first and second differences and standardized mean of absolute first and second differences. The k-Nearest Neighbor, Support Vector Machines, and Multilayer Perceptron were used as classifiers. Accuracy scores (at their highest) were 54.09%, 46.86% and 40.72% respectively, using batch cross-validation.
Index Terms:
electroencephalography, affective computing, induced emotions, affect models, academic emotions, statistical features
Citation:
Ella T. Mampusti, Jose S. Ng, Jarren James I. Quinto, Grizelda L. Teng, Merlin Teodosia C. Suarez, Rhia S. Trogo, "Measuring Academic Affective States of Students via Brainwave Signals," kse, pp.226-231, 2011 Third International Conference on Knowledge and Systems Engineering, 2011
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