- Tytuł:
- Classification of EEG Signals Using Quantum Neural Network and Cubic Spline
- Autorzy:
-
Abdul-Zahra Raheem, M.
AbdulRazzaq Hussein, E. - Powiązania:
- https://bibliotekanauki.pl/articles/227206.pdf
- Data publikacji:
- 2016
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
signals
ERP signals
cubic spline
neural networks
quantum neural network - Opis:
- The main aim of this paper is to propose Cubic Spline-Quantum Neural Network (CS-QNN) model for analysis and classification of Electroencephalogram (EEG) signals. Experimental data used here were taken from seven different electrodes. The work has been done in three stages, normalization of the signals, extracting the features by Cubic Spline Technique (CST) and classification using Quantum Neural Network (QNN). The simulation results showed that five types of EEG signals were classified with an average accuracy for seven electrodes that is 94.3% when training 70% of the features while with an average accuracy of 92.84% when training 50% of the features.
- Źródło:
-
International Journal of Electronics and Telecommunications; 2016, 62, 4; 401-408
2300-1933 - Pojawia się w:
- International Journal of Electronics and Telecommunications
- Dostawca treści:
- Biblioteka Nauki
Artykuł