Computational model of affective states profiling using commercial 14-channel wireless EEG

Many studies apply computational models for affective states profiling through brain activities manifestations which are captured using electroencephalogram (EEG) devices. Although various kinds of devices are available for research purposes, such products are not available off-the-shelf. Moreover,...

Full description

Bibliographic Details
Main Authors:	Yaacob, Hamwira Sakti, Abdul Rahman, Abdul Wahab
Format:	Conference or Workshop Item
Language:	English English
Published:	2015
Subjects:	BF511 Affection. Feeling. Emotion QA76 Computer software T Technology (General)
Online Access:	http://irep.iium.edu.my/48693/ http://irep.iium.edu.my/48693/ http://irep.iium.edu.my/48693/1/65.pdf http://irep.iium.edu.my/48693/3/hamwira.pdf

id	iium-48693
recordtype	eprints
spelling	iium-486932016-05-24T01:51:45Z http://irep.iium.edu.my/48693/ Computational model of affective states profiling using commercial 14-channel wireless EEG Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab BF511 Affection. Feeling. Emotion QA76 Computer software T Technology (General) Many studies apply computational models for affective states profiling through brain activities manifestations which are captured using electroencephalogram (EEG) devices. Although various kinds of devices are available for research purposes, such products are not available off-the-shelf. Moreover, most of EEG devices refrain users from certain movements. This becomes a challenge for capturing EEG signals during active and vibrant tasks. Thus, the aim of this study is to explore the potential of using commercial 14-channel wireless EEG for capturing the brain signals and affective states profiling through computational approach. In this study, power spectral density (PSD) is used as features. Different approaches of feature extractions are compared including the average performance of affective state classification using exclusively alpha and beta frequency bands, the average of energy density over alpha through beta frequency bands and the combination of alpha and beta power spectral density. Multilayer perceptron (MLP) neural networks are used for classification of affective states based on valence and arousal. Based on 11-fold cross validation, the classification of affective states using spectral features containing alpha and beta frequency bands produced accuracy above 80 %. In short, results have shown that 14-channel EPOC neuroheadset is viable for performing affective states profiling. 2015-10 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/48693/1/65.pdf application/pdf en http://irep.iium.edu.my/48693/3/hamwira.pdf Yaacob, Hamwira Sakti and Abdul Rahman, Abdul Wahab (2015) Computational model of affective states profiling using commercial 14-channel wireless EEG. In: 28th International Conference on Computer Applications in Industry and Engineering, 12-14 October 2015, San Diego, California, USA. http://www.caine-conf.org/2015/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	BF511 Affection. Feeling. Emotion QA76 Computer software T Technology (General)
spellingShingle	BF511 Affection. Feeling. Emotion QA76 Computer software T Technology (General) Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Computational model of affective states profiling using commercial 14-channel wireless EEG
description	Many studies apply computational models for affective states profiling through brain activities manifestations which are captured using electroencephalogram (EEG) devices. Although various kinds of devices are available for research purposes, such products are not available off-the-shelf. Moreover, most of EEG devices refrain users from certain movements. This becomes a challenge for capturing EEG signals during active and vibrant tasks. Thus, the aim of this study is to explore the potential of using commercial 14-channel wireless EEG for capturing the brain signals and affective states profiling through computational approach. In this study, power spectral density (PSD) is used as features. Different approaches of feature extractions are compared including the average performance of affective state classification using exclusively alpha and beta frequency bands, the average of energy density over alpha through beta frequency bands and the combination of alpha and beta power spectral density. Multilayer perceptron (MLP) neural networks are used for classification of affective states based on valence and arousal. Based on 11-fold cross validation, the classification of affective states using spectral features containing alpha and beta frequency bands produced accuracy above 80 %. In short, results have shown that 14-channel EPOC neuroheadset is viable for performing affective states profiling.
format	Conference or Workshop Item
author	Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab
author_facet	Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab
author_sort	Yaacob, Hamwira Sakti
title	Computational model of affective states profiling using commercial 14-channel wireless EEG
title_short	Computational model of affective states profiling using commercial 14-channel wireless EEG
title_full	Computational model of affective states profiling using commercial 14-channel wireless EEG
title_fullStr	Computational model of affective states profiling using commercial 14-channel wireless EEG
title_full_unstemmed	Computational model of affective states profiling using commercial 14-channel wireless EEG
title_sort	computational model of affective states profiling using commercial 14-channel wireless eeg
publishDate	2015
url	http://irep.iium.edu.my/48693/ http://irep.iium.edu.my/48693/ http://irep.iium.edu.my/48693/1/65.pdf http://irep.iium.edu.my/48693/3/hamwira.pdf
first_indexed	2023-09-18T21:09:01Z
last_indexed	2023-09-18T21:09:01Z
_version_	1777411127119970304

Computational model of affective states profiling using commercial 14-channel wireless EEG

Similar Items