Classification of EEG Signals Using Quantum Neural Network and Cubic Spline

被引：0
|
作者：
Raheem, Mariam Abdul-Zahra [1 ]
Hussein, Ehab AbdulRazzaq [1 ]
机构：
[1] Univ Babylon, Dept Elect Engn, Coll Engn, Hillah, Iraq
来源：
INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS | 2016年 / 62卷 / 04期
关键词：
EEG Signals; ERP Signals; Cubic Spline; Neural Networks; Quantum Neural Networks;
D O I：
10.1515/eletel-2016-0055
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
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.
引用
收藏
页码：401 / 408
页数：8
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