Emotion detection using physiological signals EEG & ECG

Emotion modeling and identification has attracted substantial interest from disciplines including computer science, cognitive science and psychology. Despite the fact that a lot of qualitative studies have been carried out on emotion, less investigated aspects include the quantifying of physiologi...

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Main Authors:	AlzeerAlhouseini, Amjad M.R., Alshaikhli, Imad Fakhri Taha, Abdul Rahman, Abdul Wahab, Dzulkifli, Mariam Adawiah
Format:	Article
Language:	English
Published:	Convergence Information Society(CIS) 2016
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/51279/ http://irep.iium.edu.my/51279/ http://irep.iium.edu.my/51279/1/IJACT-amjad.pdf

id	iium-51279
recordtype	eprints
spelling	iium-512792016-10-15T07:56:33Z http://irep.iium.edu.my/51279/ Emotion detection using physiological signals EEG & ECG AlzeerAlhouseini, Amjad M.R. Alshaikhli, Imad Fakhri Taha Abdul Rahman, Abdul Wahab Dzulkifli, Mariam Adawiah QA75 Electronic computers. Computer science Emotion modeling and identification has attracted substantial interest from disciplines including computer science, cognitive science and psychology. Despite the fact that a lot of qualitative studies have been carried out on emotion, less investigated aspects include the quantifying of physiological signals. This paper presents two physiological signals which are ECG and EEG and shows analysis of its emotional properties. A solution based on the short Fourier transform is proposed for the recognition of dynamically developing emotion patterns on ECG and EEG. Features extraction that are used in this paper are Kernel Density Estimation known as (KDE) and Mel-frequency cepstral coefficients known as MFCC. The classifier that is used in this work is Multi-layer Perceptron known as MLP, classification features are based on the valence and arousal. The experimental setup presented in this work for the elicitation of emotions is based on passive valence /arousal. The results shows that the ECG signal has direct relationship with the arousal factor rather than the valence factor. Also, EEG signal using 19 channels reported high accuracy results for determining emotions. Convergence Information Society(CIS) 2016-06-30 Article PeerReviewed application/pdf en http://irep.iium.edu.my/51279/1/IJACT-amjad.pdf AlzeerAlhouseini, Amjad M.R. and Alshaikhli, Imad Fakhri Taha and Abdul Rahman, Abdul Wahab and Dzulkifli, Mariam Adawiah (2016) Emotion detection using physiological signals EEG & ECG. International Journal of Advancements in Computing Technology (IJACT), 8 (3). pp. 103-112. ISSN 2005-8039 E-ISSN 2233-9337 http://www.globalcis.org/dl/citation.html?id=IJACT-3585
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science AlzeerAlhouseini, Amjad M.R. Alshaikhli, Imad Fakhri Taha Abdul Rahman, Abdul Wahab Dzulkifli, Mariam Adawiah Emotion detection using physiological signals EEG & ECG
description	Emotion modeling and identification has attracted substantial interest from disciplines including computer science, cognitive science and psychology. Despite the fact that a lot of qualitative studies have been carried out on emotion, less investigated aspects include the quantifying of physiological signals. This paper presents two physiological signals which are ECG and EEG and shows analysis of its emotional properties. A solution based on the short Fourier transform is proposed for the recognition of dynamically developing emotion patterns on ECG and EEG. Features extraction that are used in this paper are Kernel Density Estimation known as (KDE) and Mel-frequency cepstral coefficients known as MFCC. The classifier that is used in this work is Multi-layer Perceptron known as MLP, classification features are based on the valence and arousal. The experimental setup presented in this work for the elicitation of emotions is based on passive valence /arousal. The results shows that the ECG signal has direct relationship with the arousal factor rather than the valence factor. Also, EEG signal using 19 channels reported high accuracy results for determining emotions.
format	Article
author	AlzeerAlhouseini, Amjad M.R. Alshaikhli, Imad Fakhri Taha Abdul Rahman, Abdul Wahab Dzulkifli, Mariam Adawiah
author_facet	AlzeerAlhouseini, Amjad M.R. Alshaikhli, Imad Fakhri Taha Abdul Rahman, Abdul Wahab Dzulkifli, Mariam Adawiah
author_sort	AlzeerAlhouseini, Amjad M.R.
title	Emotion detection using physiological signals EEG & ECG
title_short	Emotion detection using physiological signals EEG & ECG
title_full	Emotion detection using physiological signals EEG & ECG
title_fullStr	Emotion detection using physiological signals EEG & ECG
title_full_unstemmed	Emotion detection using physiological signals EEG & ECG
title_sort	emotion detection using physiological signals eeg & ecg
publisher	Convergence Information Society(CIS)
publishDate	2016
url	http://irep.iium.edu.my/51279/ http://irep.iium.edu.my/51279/ http://irep.iium.edu.my/51279/1/IJACT-amjad.pdf
first_indexed	2023-09-18T21:12:35Z
last_indexed	2023-09-18T21:12:35Z
_version_	1777411351443931136

Emotion detection using physiological signals EEG & ECG

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