Emotion recognition based on EEG features in movie clips with channel selection

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dc.authorid0000-0002-9368-8902en_US
dc.authorid0000-0001-5535-4832en_US
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.contributor.authorPolat, Hasan
dc.date.accessioned2024-01-12T13:09:02Z
dc.date.available2024-01-12T13:09:02Z
dc.date.issued2017en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractEmotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain–computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems. In this study, EEG signals related to positive and negative emotions have been classified with preprocessing of channel selection. Self-Assessment Manikins was used to determine emotional states. We have employed discrete wavelet transform and machine learning techniques such as multilayer perceptron neural network (MLPNN) and k-nearest neighborhood (kNN) algorithm to classify EEG signals. The classifier algorithms were initially used for channel selection. EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined. Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. The classification performance obtained with both the algorithms are computed and compared. The average overall accuracies were obtained as 77.14 and 72.92% by using MLPNN and kNN, respectively.en_US
dc.identifier.citationÖzerdem, M. S. ve Polat, H. (2017). Emotion recognition based on EEG features in movie clips with channel selection. Brain Informatics, 4(4), 241-252.en_US
dc.identifier.doi10.1007/s40708-017-0069-3
dc.identifier.endpage252en_US
dc.identifier.issn2198-4018
dc.identifier.issue4en_US
dc.identifier.pmid28711988
dc.identifier.scopus2-s2.0-85044024171
dc.identifier.scopusqualityQ1
dc.identifier.startpage241en_US
dc.identifier.urihttps://link.springer.com/article/10.1007/s40708-017-0069-3
dc.identifier.urihttps://hdl.handle.net/11468/13187
dc.identifier.volume4en_US
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.language.isoenen_US
dc.publisherSpringer Berlin Heidelbergen_US
dc.relation.ispartofBrain Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectChannel selectionen_US
dc.subjectClassificationen_US
dc.subjectEEGen_US
dc.subjectEmotionen_US
dc.subjectWavelet transformen_US
dc.titleEmotion recognition based on EEG features in movie clips with channel selectionen_US
dc.titleEmotion recognition based on EEG features in movie clips with channel selection
dc.typeArticleen_US

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