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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
| ASCII Text | x | ||
| 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," Knowledge and Systems Engineering, International Conference on, pp. 226-231, 2011 Third International Conference on Knowledge and Systems Engineering, 2011. | |||
| BibTex | x | ||
| @article{ 10.1109/KSE.2011.43, author = {Ella T. Mampusti and Jose S. Ng and Jarren James I. Quinto and Grizelda L. Teng and Merlin Teodosia C. Suarez and Rhia S. Trogo}, title = {Measuring Academic Affective States of Students via Brainwave Signals}, journal ={Knowledge and Systems Engineering, International Conference on}, volume = {0}, year = {2011}, isbn = {978-0-7695-4567-7}, pages = {226-231}, doi = {http://doi.ieeecomputersociety.org/10.1109/KSE.2011.43}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Knowledge and Systems Engineering, International Conference on TI - Measuring Academic Affective States of Students via Brainwave Signals SN - 978-0-7695-4567-7 SP226 EP231 A1 - Ella T. Mampusti, A1 - Jose S. Ng, A1 - Jarren James I. Quinto, A1 - Grizelda L. Teng, A1 - Merlin Teodosia C. Suarez, A1 - Rhia S. Trogo, PY - 2011 KW - electroencephalography KW - affective computing KW - induced emotions KW - affect models KW - academic emotions KW - statistical features VL - 0 JA - Knowledge and Systems Engineering, International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/KSE.2011.43
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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