Classification of Electroencephalograph (EEG) Signals Using Quantum Neural Network

In this paper, quantum neural network (QNN), which is a class of feedforwardneural networks (FFNN’s), is used to recognize (EEG) signals. For thispurpose ,independent component analysis (ICA), wavelet transform (WT) andFourier transform (FT) are used as a feature extraction after normalization ofthese signals. The architecture of (QNN’s) have inherently built in fuzzy. Thehidden units of these networks develop quantized representations of thesample information provided by the training data set in various graded levelsof certainty. Experimental results presented here show that (QNN’s) arecapable of recognizing structures in data, a property that conventional(FFNN’s) with sigmoidal hidden units lack . Finally, (QNN) gave us kind of fastand realistic results compared with the (FFNN). Simulation results show that atotal classification of 81.33% for (ICA), 76.67% for (WT) and 67.33% for (FT).