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Classification of Electroencephalograph (EEG) Signals Using Quantum Neural Network
Ibtisam A. Aljazaery , Abduladhem Abdulkareem Ali, Hayder Mahdi Abdulridha
Pages - 329 - 337     |    Revised - 31-01-2011     |    Published - 08-02-2011
Volume - 4   Issue - 6    |    Publication Date - January / February  Table of Contents
Quantum Neural Network, EEG, ICA, Wavelet
In this paper, quantum neural network (QNN), which is a class of feedforward neural networks (FFNN’s), is used to recognize (EEG) signals. For this purpose ,independent component analysis (ICA), wavelet transform (WT) and Fourier transform (FT) are used as a feature extraction after normalization of these signals. The architecture of (QNN’s) have inherently built in fuzzy. The hidden units of these networks develop quantized representations of the sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that (QNN’s) are capable of recognizing structures in data, a property that conventional (FFNN’s) with sigmoidal hidden units lack . Finally, (QNN) gave us kind of fast and realistic results compared with the (FFNN). Simulation results show that a total classification of 81.33% for (ICA), 76.67% for (WT) and 67.33% for (FT).
CITED BY (4)  
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2 Tang, X., & Shu, L. (2014). A Framework of Automatic Analysis System of Electrocardiogram Signals. Int’l J. of Signal Processing, Image Processing and Pattern Recognition, 7(2), 211-222.
3 Tang, X., & Shu, L. (2014). Classification of Electrocardiogram Signals with RS and Quantum Neural Networks. Int’l J. of Multimedia and Ubiquitous Engineering, 9(2), 363-372.
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Miss Ibtisam A. Aljazaery
University of Basrah - Iraq
Professor Abduladhem Abdulkareem Ali
University of Basrah - Iraq
Dr. Hayder Mahdi Abdulridha
Babylon University - Iraq