Channel selection and feature extraction on deep EEG classification using metaheuristic and Welch PSD
Yazarlar (3)
Hitit University, Türkiye
Doç. Dr. Caner ÖZCAN
Karabük Üniversitesi, Türkiye
Bandırma Onyedi Eylül University, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Soft Computing |
| Dergi ISSN | 1432-7643 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 10-2022 |
| Cilt No | 26 |
| Sayı | 19 |
| Sayfalar | 10115 / 10125 |
| DOI Numarası | 10.1007/s00500-022-07413-0 |
| Makale Linki | https://link.springer.com/article/10.1007/s00500-022-07413-0 |
| Özet |
| Brain computer interfaces are important for different application domain such as medical, natural interfaces and entertainment. Besides the difficulty of gathering data from the human brain via different channel probs, preprocessing of data is another different and important task that must be solved in order to get better achievement. Selection of the most active channels is an important problem to achieve high classification accuracy. Metaheuristics are good solutions for selecting the optimal subset from the original set, as they have the ability to obtain an acceptable solution in a reasonable time. At the same time, it is necessary to use the correct feature extraction method so that the data can be properly represented. In addition, traditional deep learning methods used for emotion recognition ignore the spatial properties of EEG signals. This reduces the classification accuracy. In this study, we used artificial bee colony … |
| Anahtar Kelimeler |
| EEG classification | Artificial bee colony | Deep learning | Capsule networks |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 4 |
| SCOPUS | 6 |
| Google Scholar | 8 |