Emotion recognition using electroencephalogram signal

Emotion play an essential role in human’s life and it is not consciously controlled. Some of the emotion can be easily expressed by facial expressions, speech, behavior and gesture but some are not. This study investigates the emotion recognition using electroencephalogram (EEG) signal. Undoubtedly,...

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Main Authors:	Mohammad Yosi, Aqila Nur Nadira, Sidek, Khairul Azami, Yaacob, Hamwira Sakti, Othman, Marini, Jusoh, Ahmad Zamani
Format:	Article
Language:	English English
Published:	Institute of Advanced Engineering and Science 2019
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/73042/ http://irep.iium.edu.my/73042/ http://irep.iium.edu.my/73042/ http://irep.iium.edu.my/73042/7/73042%20Emotion%20recognition%20using%20electroencephalogram.pdf http://irep.iium.edu.my/73042/13/73042_Emotion%20recognition_Scopus.pdf

id	iium-73042
recordtype	eprints
spelling	iium-730422019-11-24T15:54:43Z http://irep.iium.edu.my/73042/ Emotion recognition using electroencephalogram signal Mohammad Yosi, Aqila Nur Nadira Sidek, Khairul Azami Yaacob, Hamwira Sakti Othman, Marini Jusoh, Ahmad Zamani TK7885 Computer engineering Emotion play an essential role in human’s life and it is not consciously controlled. Some of the emotion can be easily expressed by facial expressions, speech, behavior and gesture but some are not. This study investigates the emotion recognition using electroencephalogram (EEG) signal. Undoubtedly, EEG signals can detect human brain activity accurately with high resolution data acquisition device as compared to other biological signals. Changes in the human brain’s electrical activity occur very quickly, thus a high resolution device is required to determine the emotion precisely. In this study, we will prove the strength and reliability of EEG signals as an emotion recognition mechanism for four different emotions which are happy, sad, fear and calm. Data of six different subjects were collected by using BrainMarker EXG device which consist of 19 channels. The pre-processing stage was performed using second order of low pass Butterworth filter to remove the unwanted signals. Then, two ranges of frequency bands were extracted from the signals which are alpha and beta. Finally, these samples will be classified using MLP Neural Network. Classification accuracy up to 91% is achieved and the average percentage of accuracy for calm, fear, happy and sad are 83.5%, 87.3%, 85.83% and 87.6% respectively. Thus, a proof of concept, this study has been capable of proposing a system of recognizing four states of emotion which are happy, sad, fear and calm by using EEG signal. Institute of Advanced Engineering and Science 2019-08 Article PeerReviewed application/pdf en http://irep.iium.edu.my/73042/7/73042%20Emotion%20recognition%20using%20electroencephalogram.pdf application/pdf en http://irep.iium.edu.my/73042/13/73042_Emotion%20recognition_Scopus.pdf Mohammad Yosi, Aqila Nur Nadira and Sidek, Khairul Azami and Yaacob, Hamwira Sakti and Othman, Marini and Jusoh, Ahmad Zamani (2019) Emotion recognition using electroencephalogram signal. Indonesian Journal of Electrical Engineering and Computer Science, 15 (2). pp. 786-793. ISSN 2502-4752 E-ISSN 2502-4760 http://ijeecs.iaescore.com/index.php/IJEECS/article/view/18584/12867 10.11591/ijeecs.v15.i2.pp778-785
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Mohammad Yosi, Aqila Nur Nadira Sidek, Khairul Azami Yaacob, Hamwira Sakti Othman, Marini Jusoh, Ahmad Zamani Emotion recognition using electroencephalogram signal
description	Emotion play an essential role in human’s life and it is not consciously controlled. Some of the emotion can be easily expressed by facial expressions, speech, behavior and gesture but some are not. This study investigates the emotion recognition using electroencephalogram (EEG) signal. Undoubtedly, EEG signals can detect human brain activity accurately with high resolution data acquisition device as compared to other biological signals. Changes in the human brain’s electrical activity occur very quickly, thus a high resolution device is required to determine the emotion precisely. In this study, we will prove the strength and reliability of EEG signals as an emotion recognition mechanism for four different emotions which are happy, sad, fear and calm. Data of six different subjects were collected by using BrainMarker EXG device which consist of 19 channels. The pre-processing stage was performed using second order of low pass Butterworth filter to remove the unwanted signals. Then, two ranges of frequency bands were extracted from the signals which are alpha and beta. Finally, these samples will be classified using MLP Neural Network. Classification accuracy up to 91% is achieved and the average percentage of accuracy for calm, fear, happy and sad are 83.5%, 87.3%, 85.83% and 87.6% respectively. Thus, a proof of concept, this study has been capable of proposing a system of recognizing four states of emotion which are happy, sad, fear and calm by using EEG signal.
format	Article
author	Mohammad Yosi, Aqila Nur Nadira Sidek, Khairul Azami Yaacob, Hamwira Sakti Othman, Marini Jusoh, Ahmad Zamani
author_facet	Mohammad Yosi, Aqila Nur Nadira Sidek, Khairul Azami Yaacob, Hamwira Sakti Othman, Marini Jusoh, Ahmad Zamani
author_sort	Mohammad Yosi, Aqila Nur Nadira
title	Emotion recognition using electroencephalogram signal
title_short	Emotion recognition using electroencephalogram signal
title_full	Emotion recognition using electroencephalogram signal
title_fullStr	Emotion recognition using electroencephalogram signal
title_full_unstemmed	Emotion recognition using electroencephalogram signal
title_sort	emotion recognition using electroencephalogram signal
publisher	Institute of Advanced Engineering and Science
publishDate	2019
url	http://irep.iium.edu.my/73042/ http://irep.iium.edu.my/73042/ http://irep.iium.edu.my/73042/ http://irep.iium.edu.my/73042/7/73042%20Emotion%20recognition%20using%20electroencephalogram.pdf http://irep.iium.edu.my/73042/13/73042_Emotion%20recognition_Scopus.pdf
first_indexed	2023-09-18T21:43:33Z
last_indexed	2023-09-18T21:43:33Z
_version_	1777413299952943104

Emotion recognition using electroencephalogram signal

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