UMP Institutional Repository

Learner's Positive and Negative Emotion Prediction using i-Emotion

Nurshafiqa Saffah, Mohd Sharif and Rahmah, Mokhtar and Siti Normaziah, Ihsan and Azlina, Zainuddin (2016) Learner's Positive and Negative Emotion Prediction using i-Emotion. In: Proceeding of International Competition and Exhibition on Computing Innovation 2016, 6-8 December 2016 , University Sports Complex, Universiti Malaysia Pahang. pp. 271-281.. ISBN 978-967-2054-04-7

[img]
Preview
PDF
271_281.pdf

Download (643kB) | Preview

Abstract

Bio Sensor in emotion includes the use of a sensor known as Brain Computer Interface (BCI) in recognizing the emotion signals that occur in the human brain (electroencephalograph signals). The researcher used a BCI tool to collect the required data of attention and meditation value scale through a qualitative sampling. The respondent for this research are school kids’ age between 7 to 12 years old. In order to classify their positive and negative emotions, these EEG signals involves a lot of data and need to be mined in order to make it valuable and meaningful. By using rule-based (PART) classifier, the decision lists represent the regularities of the attention and meditation levels among kids. The data were generated and converted into several rule sets named rule-based prediction set and have been implemented in the i-Emotion using MATLAB environment. A baseline set which is adapted from an established eSense meter values was also coded into the prototype.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Brain Computer Interface, rule-based prediction set, baseline set, attention, meditation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 24 Mar 2017 01:44
Last Modified: 06 Feb 2018 06:14
URI: http://umpir.ump.edu.my/id/eprint/17314
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item