The convolutional neural network approach from electroencephalogram signals in emotional detection

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dc.contributor.authorTürk, Ömer
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned2021-08-05T10:29:02Z
dc.date.available2021-08-05T10:29:02Z
dc.date.issued2021en_US
dc.departmentMAÜ, Meslek Yüksekokulları, Mardin Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractAlthough brain-computer interfaces (BCI) progress rapidly, the desired success has not been achieved yet. One of these BCI is to detect emotional states in humans. An emotional state is a brain activity consisting of hormonal and mental reasons in the face of events. Emotions can be detected by electroencephalogram (EEG) signals due to these activities. Being able to detect the emotional state from EEG signals is important in terms of both time and cost. In this study, a method is proposed for the detection of the emotional state by using EEG signals. In the proposed method, we aim to classify EEG signals without any transform (Fourier transform, wavelet transform, etc.) or feature extraction method as a pre-processing. For this purpose, convolutional neural networks (CNNs) are used as classifiers, together with SEED EEG dataset containing three different emotional (positive, negative, and neutral) states. The records used in the study were taken from 15 participants in three sessions. In the proposed method, raw channel-time EEG recordings are converted into 28 × 28 size pattern segments without pre-processing. The obtained patterns are then classified in the CNN. As a result of the classification, three emotion performance averages of all participants are found to be 88.84%. Based on the participants, the highest classification performance is 93.91%, while the lowest classification performance is 77.70%. Also, the average f-score is found to be 0.88 for positive emotion, 0.87 for negative emotion, and 0.89 for neutral emotion. Likewise, the average kappa value is 0.82 for positive emotion, 0.81 for negative emotion, and 0.83 for neutral emotion. The results of the method proposed in the study are compared with the results of similar studies in the literature. We conclude that the proposed method has an acceptable level of performance.en_US
dc.identifier.scopus2-s2.0-85105192576en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85105192576&doi=10.1002%2fcpe.6356&origin=inward&txGid=dcddfad94d79ec59f34d20045fe74be1
dc.identifier.urihttps://hdl.handle.net/20.500.12514/2754
dc.identifier.wosWOS:000647892700001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherConcurrency Computationen_US
dc.relation.ispartofConcurrency Computationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectclassification, CNN, emotion, raw EEGen_US
dc.titleThe convolutional neural network approach from electroencephalogram signals in emotional detectionen_US
dc.typeArticleen_US

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