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,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
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Institute of Advanced Engineering and Science
2019
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| Online Access: | 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 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T17:29:31Z |
| format | Article |
| id | iium-73042 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:29:31Z |
| publishDate | 2019 |
| publisher | Institute of Advanced Engineering and Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 |
| 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 |
| title | 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_short | Emotion recognition using electroencephalogram signal |
| title_sort | emotion recognition using electroencephalogram signal |
| topic | TK7885 Computer engineering |
| 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 |