Emotion Classification from EEG Signals in Convolutional Neural Networks
| dc.contributor.author | Hayriye Donmez | |
| dc.contributor.author | Nalan Ǒzkurt | |
| dc.date.accessioned | 2025-10-06T17:51:20Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | The objective of this research is to classify EEG (electroencephalography) signal recordings of the subjects evoked by visual stimulus by using CNN (Convolutional Neural Networks). EEG records the electrical activity of brain signals. In medicine EEG is used to diagnose some neurological disorders but moreover the classification of the emotions is also possible from EEG recordings. Emotion recognition is an important task for the computers in machine perception. Therefore in this study the participants are presented with a video containing funny scary and sad excerpts and simultaneously EEG signal is measured by Neurosky Mindwave EEG Headset. The spectrogram of EEG signals is supplied to CNN and three emotions are classified using brain signal spectrogram images. © 2020 Elsevier B.V. All rights reserved. | |
| dc.identifier.doi | 10.1109/ASYU48272.2019.8946364 | |
| dc.identifier.isbn | 9781728128689 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078362347&doi=10.1109%2FASYU48272.2019.8946364&partnerID=40&md5=bb5880fa318c5e1790e41596393d21ee | |
| dc.identifier.uri | https://gcris.yasar.edu.tr/handle/123456789/9360 | |
| dc.language.iso | English | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | 2019 Innovations in Intelligent Systems and Applications Conference ASYU 2019 | |
| dc.subject | Cnn, Deep Learning, Eeg, Emotion Classification, Behavioral Research, Brain, Classification (of Information), Convolution, Deep Learning, Electroencephalography, Electrophysiology, Intelligent Systems, Neural Networks, Spectrographs, Convolutional Neural Network, Electrical Activities, Emotion Classification, Emotion Recognition, Machine Perception, Neurological Disorders, Signal Recording, Visual Stimulus, Biomedical Signal Processing | |
| dc.subject | Behavioral research, Brain, Classification (of information), Convolution, Deep learning, Electroencephalography, Electrophysiology, Intelligent systems, Neural networks, Spectrographs, Convolutional neural network, Electrical activities, Emotion classification, Emotion recognition, Machine perception, Neurological disorders, Signal recording, Visual stimulus, Biomedical signal processing | |
| dc.title | Emotion Classification from EEG Signals in Convolutional Neural Networks | |
| dc.type | Conference Object | |
| dspace.entity.type | Publication | |
| gdc.bip.impulseclass | C4 | |
| gdc.bip.influenceclass | C4 | |
| gdc.bip.popularityclass | C4 | |
| gdc.coar.type | text::conference output | |
| gdc.collaboration.industrial | false | |
| gdc.description.endpage | 6 | |
| gdc.description.startpage | 1 | |
| gdc.identifier.openalex | W2998529198 | |
| gdc.index.type | Scopus | |
| gdc.oaire.diamondjournal | false | |
| gdc.oaire.impulse | 23.0 | |
| gdc.oaire.influence | 4.548112E-9 | |
| gdc.oaire.isgreen | true | |
| gdc.oaire.popularity | 2.4295836E-8 | |
| gdc.oaire.publicfunded | false | |
| gdc.oaire.sciencefields | 0202 electrical engineering, electronic engineering, information engineering | |
| gdc.oaire.sciencefields | 02 engineering and technology | |
| gdc.openalex.collaboration | National | |
| gdc.openalex.fwci | 12.823 | |
| gdc.openalex.normalizedpercentile | 0.99 | |
| gdc.openalex.toppercent | TOP 1% | |
| gdc.opencitations.count | 35 | |
| gdc.plumx.crossrefcites | 18 | |
| gdc.plumx.mendeley | 68 | |
| gdc.plumx.scopuscites | 48 | |
| person.identifier.scopus-author-id | Donmez- Hayriye (57215309405), Ǒzkurt- Nalan (8546186400) | |
| relation.isOrgUnitOfPublication | ac5ddece-c76d-476d-ab30-e4d3029dee37 | |
| relation.isOrgUnitOfPublication.latestForDiscovery | ac5ddece-c76d-476d-ab30-e4d3029dee37 |
