Recognizing reader's affect using EEG data

Date of Publication


Document Type

Master's Thesis

Degree Name

Master of Science in Computer Science


College of Computer Studies


Computer Science

Thesis Adviser

Ethel Chua Joy Ong

Defense Panel Chair

Judith J. Azcarraga

Defense Panel Member

Nelson M. Marcos
Ethel Chua Joy Ong
Rafael A. Cabredo
Merlin Teodosia C. Suarez


Emotion or act is known to play vital roles in rational and intelligent behavior, such as cognition and decision-making. Detecting or recognizing act can be done by analyzing physiological data or a combination of various physiological data. The current work presents a study on brainwaves or EEG signals, which are examples of physiological data, and their association to emotions while a person is reading literary action, an unexplored domain. EEG data from 32 participants were collected while they were reading a short story. These EEG signals were collected with the use of an Emotive Insight EEG headset, attached to the head of each participant while reading the story segments presented via the developed data collector tool. After which, features were extracted and dierent datasets were built according to sex, reading preference, and reading frequency proles. Decision Trees were used to establish baseline performance results, and these were able to classify the Hourglass of Emotion model and Emotions of Literary Response models. Support Vector Machines and Multilayer Perceptrons were trained on the same datasets to see if there is an increase in performance. Results show that they indeed yielded better performance results than DT, however, only by a small degree. Principal Component Analysis was used as an approach for feature selection, and results show comparable performance as opposed to using the base feature set of all EEG features with an averaged 5 margin of error.

Abstract Format






Accession Number


Shelf Location

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

Physical Description

1 computer disc ; 4 3/4 in.


Electroencephalography; Signal processing--Digital techniques; Machine learning

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