Discrimination between genuine and acted expressions using EEG signals and machine learning

A Master of Science thesis in Biomedical Engineering by Meera Alex entitled, “Discrimination between genuine and acted expressions using EEG signals and machine learning”, submitted in April 2019. Thesis advisor is Dr. Hasan Al Nashash and thesis co-advisors are Dr. Usman Tariq and Dr. Hasan Mir....

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Alex, Meera (author)
التنسيق: doctoralThesis
منشور في: 2019
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/11073/16444
الوسوم: إضافة وسم
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author Alex, Meera
author_facet Alex, Meera
author_role author
dc.contributor.none.fl_str_mv Al Nashash, Hasan
Tariq, Usman
Mir, Hasan
dc.creator.none.fl_str_mv Alex, Meera
dc.date.none.fl_str_mv 2019-05-23T09:43:45Z
2019-05-23T09:43:45Z
2019-04
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv 35.232-2019.12
http://hdl.handle.net/11073/16444
dc.language.none.fl_str_mv en_US
dc.subject.none.fl_str_mv Empirical mode decomposition
Discrete wavelet transform
Emotions
Electroencephalogram
Electroencephalography
Emotions
dc.title.none.fl_str_mv Discrimination between genuine and acted expressions using EEG signals and machine learning
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/doctoralThesis
description A Master of Science thesis in Biomedical Engineering by Meera Alex entitled, “Discrimination between genuine and acted expressions using EEG signals and machine learning”, submitted in April 2019. Thesis advisor is Dr. Hasan Al Nashash and thesis co-advisors are Dr. Usman Tariq and Dr. Hasan Mir.
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language_invalid_str_mv en_US
network_acronym_str aus
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oai_identifier_str oai:repository.aus.edu:11073/16444
publishDate 2019
repository.mail.fl_str_mv
repository.name.fl_str_mv
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spelling Discrimination between genuine and acted expressions using EEG signals and machine learningAlex, MeeraEmpirical mode decompositionDiscrete wavelet transformEmotionsElectroencephalogramElectroencephalographyEmotionsA Master of Science thesis in Biomedical Engineering by Meera Alex entitled, “Discrimination between genuine and acted expressions using EEG signals and machine learning”, submitted in April 2019. Thesis advisor is Dr. Hasan Al Nashash and thesis co-advisors are Dr. Usman Tariq and Dr. Hasan Mir.The main purpose of this thesis work was to quantify happiness in an objective manner. This is in line with the objectives of the National Program for Happiness and Positivity in the UAE. The major contribution to this thesis work included designing and conducting experiments to study the emotion-related cognitive process using EEG signals. The focus is to develop a novel method for classifying EEG signals related to genuine and acted expressions. A framework for quantifying three different affective states: actual/true positive, acted/fake and neutral positive emotions were developed. The major stages involved the development of an emotion related EEG database comprising of 28 subjects, feature extraction, and finally the application of machine learning algorithms. Two main approaches were used for feature extraction: the first method included discrete wavelet transform while, the second method involved a combination of discrete wavelet transform (DWT) and empirical mode decomposition (EMD). Average power features extracted from both the techniques were used for classification of the three affective states. Highest accuracy of 69.2 % using the DWT method and 94.2 % using DWT-EMD method was achieved.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME)Al Nashash, HasanTariq, UsmanMir, Hasan2019-05-23T09:43:45Z2019-05-23T09:43:45Z2019-04info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2019.12http://hdl.handle.net/11073/16444en_USoai:repository.aus.edu:11073/164442025-06-26T12:20:42Z
spellingShingle Discrimination between genuine and acted expressions using EEG signals and machine learning
Alex, Meera
Empirical mode decomposition
Discrete wavelet transform
Emotions
Electroencephalogram
Electroencephalography
Emotions
status_str publishedVersion
title Discrimination between genuine and acted expressions using EEG signals and machine learning
title_full Discrimination between genuine and acted expressions using EEG signals and machine learning
title_fullStr Discrimination between genuine and acted expressions using EEG signals and machine learning
title_full_unstemmed Discrimination between genuine and acted expressions using EEG signals and machine learning
title_short Discrimination between genuine and acted expressions using EEG signals and machine learning
title_sort Discrimination between genuine and acted expressions using EEG signals and machine learning
topic Empirical mode decomposition
Discrete wavelet transform
Emotions
Electroencephalogram
Electroencephalography
Emotions
url http://hdl.handle.net/11073/16444