Emotion recognition based on spatially smooth spectral features of the EEG
| dc.contributor.author | Ballı, Tuğçe | |
| dc.contributor.author | Deniz, Sencer M. | |
| dc.contributor.author | Cebeci, Bora | |
| dc.contributor.author | Erbey, Miray | |
| dc.contributor.author | Duru, Adil Deniz | |
| dc.contributor.author | Demiralp, Tamer | |
| dc.date.accessioned | 2021-05-15T12:41:02Z | |
| dc.date.available | 2021-05-15T12:41:02Z | |
| dc.date.issued | 2013 | |
| dc.department | Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description | 6th International IEEE EMBS Conference on Neural Engineering (NER) -- NOV 06-08, 2013 -- San Diego, CA | |
| dc.description | Duru, Adil Deniz/0000-0003-3014-9626; Demiralp, Tamer/0000-0002-6803-734X | |
| dc.description.abstract | The primary aim of this study was to select the optimal feature subset for discrimination of three dimensions of emotions (arousal, valence, liking) from subjects using electroencephalogram (EEG) signals. The EEG signals were collected from 25 channels on 21 healthy subjects whilst they were watching movie segments with emotional content. The band power values extracted from eleven frequency bands, namely delta (0.5-3.5 Hz), theta (4-7.5 Hz), alpha (8-12 Hz), beta (13-30 Hz), gamma (30-50 Hz), low theta (4-6 Hz), high theta (6-8 Hz), low alpha (8-10 Hz), high alpha (10-12 Hz), low beta (13-18 Hz) and high beta (18-30 Hz) bands, were used as EEG features. The most discriminative features for classification of EEG feature sets were selected using sequential floating forward search (SFFS) algorithm and a modified version of SFFS algorithm, which imposes the topographical smoothness of spectral features, along with linear discriminant analysis (LDA) classifier. The best classification accuracies for three emotional dimensions were obtained for liking (72.22%) followed by arousal (67.50%) and valence (66.67%). SFFS-LDA and modified SFFS-LDA algorithms produced slightly different classification accuracies. However, the findings suggested that the use of modified SFFS-LDA algorithm provides more robust feature subsets for understanding of underlying functional neuroanatomic mechanisms corresponding to distinct emotional states. | en_US |
| dc.description.sponsorship | IEEE EMBS, Battelle, Brain Vis LLC, Cortech Solut, Univ Minnesota, IGERT Program, Syst Neuroengineering, IOP Publishing, Ripple, Univ Minnesota, Inst Engn Med, Plexon | en_US |
| dc.identifier.endpage | 410 | en_US |
| dc.identifier.isbn | 978-1-4673-1969-0 | |
| dc.identifier.issn | 1948-3546 | |
| dc.identifier.scopus | 2-s2.0-84897674242 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.startpage | 407 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/734 | |
| dc.identifier.wos | WOS:000331259200103 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Ballı, Tuğçe | |
| dc.language.iso | en | |
| dc.publisher | Ieee | en_US |
| dc.relation.ispartof | 2013 6Th International Ieee/Embs Conference on Neural Engineering (Ner) | |
| dc.relation.ispartofseries | International IEEE EMBS Conference on Neural Engineering | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | EEG | en_US |
| dc.subject | Emotion Recognition | en_US |
| dc.subject | Smooth Spectral | en_US |
| dc.title | Emotion recognition based on spatially smooth spectral features of the EEG | |
| dc.type | Conference Object |
