ANN based classification of EEG signals using the average power based on rectangle approximation window
Yazarlar (1)
Prof. Dr. Mahmut HEKİM
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Przeglad Elektrotechniczny |
| Dergi ISSN | 0033-2097 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q4 |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2012 |
| Cilt No | 88 |
| Sayı | 8 |
| Sayfalar | 210 / 215 |
| Makale Linki | http://pe.org.pl/issue.php?lang=1&num=08/2012 |
| Özet |
| In this study, EEG signals were classified by using the average powers extracted by means of the rectangle approximation window based average power method from the power spectral densities of frequency sub-bands of the signals and two different artificial neural networks (ANNs) which are adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron neural network (MLPNN). In order to evaluate their performances together the proposed approach, four different experiments were implemented by using different mixtures of classes. The experiments showed that both classifiers with the proposed approach resulted in satisfactory classification accuracy rates, although the success of MLPNN classifier was a little better than the other. |
| Anahtar Kelimeler |
| Artificial neural networks | Discrete wavelet transform (DWT) | EEG signals | Power spectral density (PSD) | Rectangle approximation window based average power |
