Classification of Electroencephalograph (EEG) Signals Using Quantum Neural Network

Signal Processing: An International Journal

Ibtisam A. Aljazaery¹  Hayder Mahdi Abdulridha¹  Abduladhem Abdulkareem Ali¹ 

[1] $$

关键词: Quantum Neural Network;    EEG;    ICA;    Wavelet;   

DOI  :  

学科分类：物理（综合）

来源: Computer Science Journals

【 摘 要 】

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).

【 授权许可】

Unknown