Capturing Data of Children’ Concentration and Meditation Levels : How Learners’ Effort Influence the Academic Emotional Response Level

Emotions play a key role in non-verbal communication, accompany everyone in daily life, and are essential in understanding human behavior. Emotion recognition can be elicited from text, speech, facial expression as well as from body gesture. This study aims to assess the use of bio signal analy...

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Bibliographic Details
Main Authors: Nurshafiqa, Sharif, Rahmah, Mokhtar, Siti Normaziah, Ihsan, Azlina, Zainuddin, Nor Azan, Mat Zin
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/11176/
http://umpir.ump.edu.my/id/eprint/11176/1/Capturing%20Data%20of%20Children%E2%80%99%20Concentration%20and%20Meditation%20Levels-%20How%20Learners%E2%80%99%20Effort%20Influence%20the%20Academic%20Emotional%20Response%20Level.pdf
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Summary:Emotions play a key role in non-verbal communication, accompany everyone in daily life, and are essential in understanding human behavior. Emotion recognition can be elicited from text, speech, facial expression as well as from body gesture. This study aims to assess the use of bio signal analysis from electroencephalogram (EEG) to measure emotional response of a human subject while learning. This study focused on recognition of inner emotions since humans can control their facial expressions or vocal intonation. A noninvasive brain-computer interfaces (BCI) that read brain signals is used to detect brain waves and transmit them to a computer for further data processing and analysis. Different dataset from the subjects were collected where all the subjects underwent two different when they underwent two different learning sessions with two trials each. The subjects level of concentration and meditation were captured during the sessions in order to study their academic emotional response level. Results show that the trend of the time recorded for each subject while completing each task were decreased . This suggests that it is possible to obtain faster and more accurate brain wave control with experience and practices. Implication from the study is for modeling users in intelligent tutoring systems.