Classification of EEG Signals Using Quantum Neural Network and Cubic Spline

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 sign...

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Bibliographic Details
Published in:International Journal of Electronics and Telecommunications 2016-12, Vol.62 (4), p.401-408
Main Authors: Raheem, Mariam Abdul-Zahra, Hussein, Ehab AbdulRazzaq
Format: Article
Language:English
Subjects:
Cubic Spline
EEG Signals
ERP Signals
Neural Networks
Quantum Neural Networks
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Summary: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.
ISSN:2300-1933
2081-8491
2300-1933
DOI:10.1515/eletel-2016-0055