Affect modeling in POOLE III using EEG signals and facial features

Date of Publication

2011

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Merlin Teodosia C. Suarez

Defense Panel Chair

Ethel C. Ong

Defense Panel Member

Florante R. Salvador
Rhia S. Trogo

Abstract/Summary

In the field emotion detection, cameras and psychological sensors are some mediums used to gather data. ITS developers have created ITS with detailed model which makes an affective gap between machine and the learner. The Programmers Object-Oriented Learning Environment III (POOLE III) is a system that teaches object-oriented programming to students. However, POOLE III lacks an interactive avatar that will help further the student in learning or studying. This thesis presents an affective emotion detection module incorporated into POOLE III to give the student a more interactive learning environment and provide feedbacks to the student. Classification algorithm such as Naïve Bayes, Bayes Network, KNN and C4.5 were used to train the facial features (x and y coordinates of facial points), EEG features and both. Both facial and EEG features achieved the highest accuracies in all classification algorithms. Naïve Bayes achieved 48.82%, Bayes Network achieved 89.31%, kNN with k=3 achived 95.23%, and C4.5 achieved 95.73% using facial and EEG features. The research was able to detect the emotions in a learning context.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU18562

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

1v. various foliations; illustrations (some colored) ; 28 cm.

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