Emotional profiling through supervised machine learning of interrupted EEG interpolation

It has been reported that the construction of emotion profiling models using supervised machine learning involves data acquisition, signal pre-processing, feature extraction and classification. However, almost all papers do not address the issue of profiling emotion using supervised machine learning...

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Main Authors:	Yaacob, Hamwira Sakti, Omar, Hazim, Handayani, Dini, Hassan, Raini
Format:	Article
Language:	English
Published:	ACCENTS JOURNAL 2019
Subjects:	BF511 Affection. Feeling. Emotion T58.5 Information technology
Online Access:	http://irep.iium.edu.my/75492/ http://irep.iium.edu.my/75492/ http://irep.iium.edu.my/75492/ http://irep.iium.edu.my/75492/1/6.pdf

id	iium-75492
recordtype	eprints
spelling	iium-754922019-11-11T10:27:48Z http://irep.iium.edu.my/75492/ Emotional profiling through supervised machine learning of interrupted EEG interpolation Yaacob, Hamwira Sakti Omar, Hazim Handayani, Dini Hassan, Raini BF511 Affection. Feeling. Emotion T58.5 Information technology It has been reported that the construction of emotion profiling models using supervised machine learning involves data acquisition, signal pre-processing, feature extraction and classification. However, almost all papers do not address the issue of profiling emotion using supervised machine learning on the interrupted encephalogram (EEG) signals. Based on a preliminary study, emotion profiling on interrupted EEG signals produces low classification accuracy, using multilayer perceptron (MLP). Furthermore, lower emotion classification accuracy is produced from interrupted EEG signals with higher number of segments. Thus, the objective of this paper is to propose a technique and present the outcomes of handling interrupted EEG signals for emotion profiling. This is done by the suppression and interpolation of originally interrupted EEG signals at pre-process stage. As a result, emotion classification using MLP on interpolated data improves from 80.1% to 95%. ACCENTS JOURNAL 2019-07 Article PeerReviewed application/pdf en cc_by http://irep.iium.edu.my/75492/1/6.pdf Yaacob, Hamwira Sakti and Omar, Hazim and Handayani, Dini and Hassan, Raini (2019) Emotional profiling through supervised machine learning of interrupted EEG interpolation. International Journal of Advanced Computer Research, 9 (43). pp. 242-251. ISSN 2249-7277 E-ISSN 2277-7970 https://www.accentsjournals.org/journals.php?journalsId=103 10.19101/IJACR.PID17
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	BF511 Affection. Feeling. Emotion T58.5 Information technology
spellingShingle	BF511 Affection. Feeling. Emotion T58.5 Information technology Yaacob, Hamwira Sakti Omar, Hazim Handayani, Dini Hassan, Raini Emotional profiling through supervised machine learning of interrupted EEG interpolation
description	It has been reported that the construction of emotion profiling models using supervised machine learning involves data acquisition, signal pre-processing, feature extraction and classification. However, almost all papers do not address the issue of profiling emotion using supervised machine learning on the interrupted encephalogram (EEG) signals. Based on a preliminary study, emotion profiling on interrupted EEG signals produces low classification accuracy, using multilayer perceptron (MLP). Furthermore, lower emotion classification accuracy is produced from interrupted EEG signals with higher number of segments. Thus, the objective of this paper is to propose a technique and present the outcomes of handling interrupted EEG signals for emotion profiling. This is done by the suppression and interpolation of originally interrupted EEG signals at pre-process stage. As a result, emotion classification using MLP on interpolated data improves from 80.1% to 95%.
format	Article
author	Yaacob, Hamwira Sakti Omar, Hazim Handayani, Dini Hassan, Raini
author_facet	Yaacob, Hamwira Sakti Omar, Hazim Handayani, Dini Hassan, Raini
author_sort	Yaacob, Hamwira Sakti
title	Emotional profiling through supervised machine learning of interrupted EEG interpolation
title_short	Emotional profiling through supervised machine learning of interrupted EEG interpolation
title_full	Emotional profiling through supervised machine learning of interrupted EEG interpolation
title_fullStr	Emotional profiling through supervised machine learning of interrupted EEG interpolation
title_full_unstemmed	Emotional profiling through supervised machine learning of interrupted EEG interpolation
title_sort	emotional profiling through supervised machine learning of interrupted eeg interpolation
publisher	ACCENTS JOURNAL
publishDate	2019
url	http://irep.iium.edu.my/75492/ http://irep.iium.edu.my/75492/ http://irep.iium.edu.my/75492/ http://irep.iium.edu.my/75492/1/6.pdf
first_indexed	2023-09-18T21:46:49Z
last_indexed	2023-09-18T21:46:49Z
_version_	1777413505485373440

Emotional profiling through supervised machine learning of interrupted EEG interpolation

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