Title

Building an improved emotion recognition system for affective learning via brainwaves signals

Date of Publication

2014

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science

Subject Categories

Artificial Intelligence and Robotics | Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Merlin Teodosia Suarez

Defense Panel Chair

Ethel C. Ong

Defense Panel Member

Rafael Cabredo
Jocelyn Cu

Abstract/Summary

Multiple studies show that emotions can be extracted from Electroencephalogram (EEG) signals. In order to achieve a high recognition rate, feature extraction techniques must be properly applied when working with brainwave signals. Of these techniques, the more commonly used are statistical features and Fast Fourier transform. Such feature extraction however, was only able to achieve the highest recognition rate of 67.

Abstract Format

html

Language

English

Format

Electronic

Accession Number

CDTU019254

Shelf Location

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

Physical Description

1 computer disc ; 4 3/4 in.

Keywords

Electroencephalography; Theta rhythm; Fourier transformations

Embargo Period

5-2-2021

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