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Discrimination of psychotic symptoms from controls through data mining methods based on emotional principle components

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dc.contributor.authorMaras, Abdullah
dc.contributor.authorAydın, Serap
dc.contributor.editorBadnjevic, A.
dc.contributor.institutionMaras, Abdullah, Istanbul Üniversitesi, Istanbul, Turkey
dc.contributor.institutionAydın, Serap, Department of Biomedical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey
dc.date.accessioned2025-10-05T16:21:10Z
dc.date.issued2017
dc.description.abstractIn this study, different data mining techniques has been used for classification of healthy controls and patients diagnosed by First Episode Psychosis with respect to complexity of frequency band activities (Delta, Theta, Alpha, Beta, Gamma)in multi channel EEG measurements mediated by emotional, static and visual stimuli including affective pictures from IAPS. Degree of local EEG complexity has been correlated by largeness of the dominant principle component in each EEG sub-band. The best classification performances are provided by Rotation Forest, Simple Logistic and Artificial Neural Networks when the components from occipito-parietal and posterio-temporal locations (P3, P4, O1, O2, T5 and T6) are considered as features in Gamma with respect to neutral emotional state. © 2018 Elsevier B.V., All rights reserved.
dc.description.sponsorshipErkona doo Sarajevo, Bosnia and Herzegovina
dc.description.sponsorshipet al.
dc.description.sponsorshipOracle Bosnia and Herzegovina
dc.description.sponsorshipPrivredna banka dd Sarajevo, Bosnia and Herzegovina
dc.description.sponsorshipSymphony Sarajevo, Bosnia and Herzegovina
dc.description.sponsorshipVerlab doo Sarajevo, Bosnia and Herzegovina
dc.identifier.conferenceNameInternational Conference on Medical and Biological Engineering, CMBEBIH 2017
dc.identifier.conferencePlaceSarajevo
dc.identifier.doi10.1007/978-981-10-4166-2_5
dc.identifier.endpage30
dc.identifier.issn16800737
dc.identifier.issn14339277
dc.identifier.scopus2-s2.0-85016012858
dc.identifier.startpage26
dc.identifier.urihttps://doi.org/10.1007/978-981-10-4166-2_5
dc.identifier.urihttps://hdl.handle.net/20.500.14719/12242
dc.identifier.volume62
dc.language.isoen
dc.publisherSpringer Verlag service@springer.de
dc.relation.sourceIFMBE Proceedings
dc.subject.authorkeywordsClassification Emotion
dc.subject.authorkeywordsData Mining
dc.subject.authorkeywordsElectroencephalography
dc.subject.authorkeywordsBiochemical Engineering
dc.subject.authorkeywordsComplex Networks
dc.subject.authorkeywordsElectroencephalography
dc.subject.authorkeywordsElectrophysiology
dc.subject.authorkeywordsNeural Networks
dc.subject.authorkeywordsPatient Monitoring
dc.subject.authorkeywordsClassification Performance
dc.subject.authorkeywordsData Mining Methods
dc.subject.authorkeywordsEeg Complexity
dc.subject.authorkeywordsEmotional State
dc.subject.authorkeywordsHealthy Controls
dc.subject.authorkeywordsPrinciple Component
dc.subject.authorkeywordsRotation Forests
dc.subject.authorkeywordsVisual Stimulus
dc.subject.authorkeywordsData Mining
dc.subject.indexkeywordsBiochemical engineering
dc.subject.indexkeywordsComplex networks
dc.subject.indexkeywordsElectroencephalography
dc.subject.indexkeywordsElectrophysiology
dc.subject.indexkeywordsNeural networks
dc.subject.indexkeywordsPatient monitoring
dc.subject.indexkeywordsClassification performance
dc.subject.indexkeywordsData mining methods
dc.subject.indexkeywordsEEG complexity
dc.subject.indexkeywordsEmotional state
dc.subject.indexkeywordsHealthy controls
dc.subject.indexkeywordsPrinciple component
dc.subject.indexkeywordsRotation forests
dc.subject.indexkeywordsVisual stimulus
dc.subject.indexkeywordsData mining
dc.titleDiscrimination of psychotic symptoms from controls through data mining methods based on emotional principle components
dc.typeConference Paper
dcterms.referencesGoyal, Money, Classification of emotions based on ERP feature extraction, pp. 660-662, (2016), Data Mining Practical Machine Learning Tools and Techniques, (2005), AlZoubi, Omar A., Classification of EEG for affect recognition: An adaptive approach, Lecture Notes in Computer Science, 5866 LNAI, pp. 52-61, (2009), Goshvarpour, Ateke, Dynamical analysis of emotional states from electroencephalogram signals, Biomedical Engineering - Applications, Basis and Communications, 28, 2, (2016), Schuster, Timo, EEG - Pattern classification during emotional picture processing, ACM International Conference Proceeding Series, (2010), Mehmood, Raja Majid, Emotion classification of EEG brain signal using SVM and KNN, (2015), Sohaib, Ahmad Tauseef, Evaluating classifiers for emotion recognition using EEG, Lecture Notes in Computer Science, 8027 LNAI, pp. 492-501, (2013), Conneau, Anne Claire, Assessment of new spectral features for eeg-based emotion recognition, Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4698-4702, (2014), Bhardwaj, Aayush, Classification of human emotions from EEG signals using SVM and LDA Classifiers, pp. 180-185, (2015), Aydın, Serap, Emotion recognition with eigen features of frequency band activities embedded in induced brain oscillations mediated by affective pictures, International Journal of Neural Systems, 26, 3, (2016)
dspace.entity.typePublication
local.indexed.atScopus
person.identifier.scopus-author-id57193645180
person.identifier.scopus-author-id24460465000

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