Enhanced deep capsule network for EEG-based emotion recognition
Yazarlar (2)
Dr. Öğr. Üyesi Hüseyin Çizmeci Hitit Üniversitesi, Türkiye
Doç. Dr. Caner ÖZCAN Karabük Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Signal Image and Video Processing (Q3) | ||
| Dergi ISSN | 1863-1703 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2023 |
| Cilt / Sayı / Sayfa | 17 / 2 / 463–469 | DOI | 10.1007/s11760-022-02251-x |
| Makale Linki | https://link.springer.com/article/10.1007/s11760-022-02251-x | ||
| UAK Araştırma Alanları |
Yapay Zeka
Biyoenformatik
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| Özet |
| Recently, it has become very popular to use electroencephalogram (EEG) signals in emotion recognition studies. But, EEG signals are much more complex than image and audio signals. There may be inconsistencies even in signals recorded from the same person. Therefore, EEG signals obtained from the human brain must be analyzed and processed accurately and consistently. In addition, traditional algorithms used to classify emotion ignore the neighborhood relationship and hierarchical order within the EEG signals. In this paper, a method including selection of suitable channels from EEG data, feature extraction by Welch power spectral density estimation of selected channels and enhanced capsule network-based classification model is presented. The most important innovation of the method is to adjust the architecture of the capsule network to adapt to the EEG signals. Thanks to the proposed method, 99 … |
| Anahtar Kelimeler |
| Capsule network | Deep learning | EEG | Emotion recognition | Feature extraction |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 15 |
| Scopus | 16 |
| Google Scholar | 24 |