Title :
Measuring Academic Affective States of Students via Brainwave Signals
Author :
Mampusti, Ella T. ; Ng, Jose S. ; Quinto, Jarren James I ; Teng, Grizelda L. ; Suarez, Merlin Teodosia C ; Trogo, Rhia S.
Author_Institution :
Center for Emphatic Human-Comput. Interaction, De La Salle Univ., Manila, Philippines
Abstract :
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.
Keywords :
Butterworth filters; electroencephalography; multilayer perceptrons; Berg card sorting task; Butterworth band pass filter; EEG signal; academic affective states; batch cross-validation; brainwave signals; electroencephalogram signal; human academic emotion; k-nearest neighbor; multilayer perceptron; noise reduction; student; support vector machines; Accuracy; Brain modeling; Classification algorithms; Educational institutions; Electroencephalography; Feature extraction; Support vector machines; academic emotions; affect models; affective computing; electroencephalography; induced emotions; statistical features;
Conference_Titel :
Knowledge and Systems Engineering (KSE), 2011 Third International Conference on
Conference_Location :
Hanoi
Print_ISBN :
978-1-4577-1848-9
DOI :
10.1109/KSE.2011.43
