Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification

Abstract Recognizing emotions using biological brain signals requires accurate and efficient signal processing and feature extraction methods. Existing methods use several techniques to extract useful features from a fixed number of electroencephalography (EEG) channels. The primary objective of thi...

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Autores principales:	Sofien Gannouni, Arwa Aledaily, Kais Belwafi, Hatim Aboalsamh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/39b5dd32cea54af3a694bb52d1e199b2
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id	oai:doaj.org-article:39b5dd32cea54af3a694bb52d1e199b2
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spelling	oai:doaj.org-article:39b5dd32cea54af3a694bb52d1e199b22021-12-02T13:26:50ZEmotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification10.1038/s41598-021-86345-52045-2322https://doaj.org/article/39b5dd32cea54af3a694bb52d1e199b22021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86345-5https://doaj.org/toc/2045-2322Abstract Recognizing emotions using biological brain signals requires accurate and efficient signal processing and feature extraction methods. Existing methods use several techniques to extract useful features from a fixed number of electroencephalography (EEG) channels. The primary objective of this study was to improve the performance of emotion recognition using brain signals by applying a novel and adaptive channel selection method that acknowledges that brain activity has a unique behavior that differs from one person to another and one emotional state to another. Moreover, we propose identifying epochs, which are the instants at which excitation is maximum, during the emotion to improve the system’s accuracy. We used the zero-time windowing method to extract instantaneous spectral information using the numerator group-delay function to accurately detect the epochs in each emotional state. Different classification scheme were defined using QDC and RNN and evaluated using the DEAP database. The experimental results showed that the proposed method is highly competitive compared with existing studies of multi-class emotion recognition. The average accuracy rate exceeded 89%. Compared with existing algorithms dealing with 9 emotions, the proposed method enhanced the accuracy rate by 8%. Moreover, experiment shows that the proposed system outperforms similar approaches discriminating between 3 and 4 emotions only. We also found that the proposed method works well, even when applying conventional classification algorithms.Sofien GannouniArwa AledailyKais BelwafiHatim AboalsamhNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sofien Gannouni Arwa Aledaily Kais Belwafi Hatim Aboalsamh Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification
description	Abstract Recognizing emotions using biological brain signals requires accurate and efficient signal processing and feature extraction methods. Existing methods use several techniques to extract useful features from a fixed number of electroencephalography (EEG) channels. The primary objective of this study was to improve the performance of emotion recognition using brain signals by applying a novel and adaptive channel selection method that acknowledges that brain activity has a unique behavior that differs from one person to another and one emotional state to another. Moreover, we propose identifying epochs, which are the instants at which excitation is maximum, during the emotion to improve the system’s accuracy. We used the zero-time windowing method to extract instantaneous spectral information using the numerator group-delay function to accurately detect the epochs in each emotional state. Different classification scheme were defined using QDC and RNN and evaluated using the DEAP database. The experimental results showed that the proposed method is highly competitive compared with existing studies of multi-class emotion recognition. The average accuracy rate exceeded 89%. Compared with existing algorithms dealing with 9 emotions, the proposed method enhanced the accuracy rate by 8%. Moreover, experiment shows that the proposed system outperforms similar approaches discriminating between 3 and 4 emotions only. We also found that the proposed method works well, even when applying conventional classification algorithms.
format	article
author	Sofien Gannouni Arwa Aledaily Kais Belwafi Hatim Aboalsamh
author_facet	Sofien Gannouni Arwa Aledaily Kais Belwafi Hatim Aboalsamh
author_sort	Sofien Gannouni
title	Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification
title_short	Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification
title_full	Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification
title_fullStr	Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification
title_full_unstemmed	Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification
title_sort	emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/39b5dd32cea54af3a694bb52d1e199b2
work_keys_str_mv	AT sofiengannouni emotiondetectionusingelectroencephalographysignalsandazerotimewindowingbasedepochestimationandrelevantelectrodeidentification AT arwaaledaily emotiondetectionusingelectroencephalographysignalsandazerotimewindowingbasedepochestimationandrelevantelectrodeidentification AT kaisbelwafi emotiondetectionusingelectroencephalographysignalsandazerotimewindowingbasedepochestimationandrelevantelectrodeidentification AT hatimaboalsamh emotiondetectionusingelectroencephalographysignalsandazerotimewindowingbasedepochestimationandrelevantelectrodeidentification
_version_	1718393024090734592

Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification

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