Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning
A Master of Science thesis in Biomedical Engineering by Mostafa Mohamed Moussa entitled, “Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning”, submitted in April 2020. Thesis advisors are Dr. Usman Tariq and Dr. Hasan Al Nashash. Soft copy is available (Thesis, Approval...
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| Format: | doctoralThesis |
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2020
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| Online Access: | http://hdl.handle.net/11073/16713 |
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| _version_ | 1864513433283592192 |
|---|---|
| author | Moussa, Mostafa Mohamed |
| author_facet | Moussa, Mostafa Mohamed |
| author_role | author |
| dc.contributor.none.fl_str_mv | Tariq, Usman Al Nashash, Hasan |
| dc.creator.none.fl_str_mv | Moussa, Mostafa Mohamed |
| dc.date.none.fl_str_mv | 2020-06-21T06:34:32Z 2020-06-21T06:34:32Z 2020-04 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2020.05 http://hdl.handle.net/11073/16713 |
| dc.language.none.fl_str_mv | en_US |
| dc.subject.none.fl_str_mv | Electroencephalogram Machine learning Support vector machines Deep learning Artificial neural networks Convolutional neural networks Subject-dependent analysis subject-independent analysis |
| dc.title.none.fl_str_mv | Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep 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 Mostafa Mohamed Moussa entitled, “Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning”, submitted in April 2020. Thesis advisors are Dr. Usman Tariq and Dr. Hasan Al Nashash. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_2b0523905187ae20a1e91bfcd1c79729 |
| identifier_str_mv | 35.232-2020.05 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/16713 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep LearningMoussa, Mostafa MohamedElectroencephalogramMachine learningSupport vector machinesDeep learningArtificial neural networksConvolutional neural networksSubject-dependent analysissubject-independent analysisA Master of Science thesis in Biomedical Engineering by Mostafa Mohamed Moussa entitled, “Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning”, submitted in April 2020. Thesis advisors are Dr. Usman Tariq and Dr. Hasan Al Nashash. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).Genuineness of smiles is one aspect of the field of deception recognition, one that is prevalent in myriad social situations, and it is not easy to tell when a person’s smile is genuine or not for the average person. Machine learning techniques, such as support vector machines or artificial neural networks, can allow better distinction between fake and real smiles by making use of electroencephalograms (EEG) from subjects with a simple experimental protocol, in which the subject’s response is known by the experimenters. Machine learning techniques were previously used in affect recognition, though not for distinguishing real and fake smiles through EEG signals. The objective of this study is to distinguish between fake and real smiles using deep learning techniques, more specifically shallow neural networks, convolutional neural networks, and support vector machines (SVMs) as a baseline from EEG signals. The experimental approach involved presenting subjects with visual stimuli and recording their physical response and their EEG, which was used with the aforementioned algorithms. The SVM classifier used the radial basis function kernel, with optimized parameters, the simple neural network was a three-layer pattern recognition network with 150 hidden units using scaled conjugate gradient as the training function, the convolutional neural networks used stochastic gradient descent with a momentum of 0.95 for all the different architectures, and the optimal one was selected based on the results. The accuracies of the simple neural network, convolutional neural network, and SVM are 88.879 %, 90.446 %, and 48.387 % respectively for subject-dependent classification, and the convolutional neural network yielded 53.418 % for subject-independent classification.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME)Tariq, UsmanAl Nashash, Hasan2020-06-21T06:34:32Z2020-06-21T06:34:32Z2020-04info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2020.05http://hdl.handle.net/11073/16713en_USoai:repository.aus.edu:11073/167132025-06-26T12:24:17Z |
| spellingShingle | Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning Moussa, Mostafa Mohamed Electroencephalogram Machine learning Support vector machines Deep learning Artificial neural networks Convolutional neural networks Subject-dependent analysis subject-independent analysis |
| status_str | publishedVersion |
| title | Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning |
| title_full | Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning |
| title_fullStr | Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning |
| title_full_unstemmed | Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning |
| title_short | Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning |
| title_sort | Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning |
| topic | Electroencephalogram Machine learning Support vector machines Deep learning Artificial neural networks Convolutional neural networks Subject-dependent analysis subject-independent analysis |
| url | http://hdl.handle.net/11073/16713 |