Classification of EEG Signals Using Quantum Neural Network and Cubic Spline
Journal Title: International Journal of Electronics and Telecommunications - Year 2016, Vol 62, Issue 4
Abstract
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.
Authors and Affiliations
Mariam Abdul-Zahra Raheem, Ehab AbdulRazzaq Hussein
Keywords
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- EP ID EP200979
- DOI 10.1515/eletel-2016-0055
- Views 113
- Downloads 0
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