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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Hassan Muwafaq Gheni
Computer Techniques Engineering Department, Al-Mustaqbal University college, Hillah 51001, Iraq
Computer Science & IT Electrical & Electronics Engineering

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Classification of EEG Signal by Using Optimized Quantum Neural Network Dalael Saad Abdul-Zahra; Ali Talib Jawad; Hassan Muwafaq Gheni; Ali Najim Abdullah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3486

Abstract

In recent years the algorithms of machine learning was used for brain signals identifing which is a useful technique for diagnosing diseases like Alzheimer's and epilepsy. In this paper, the Electroencephalogram (EEG) signals are classified using an optimized Quantum neural network (QNN) after normalizing these signals, wavelet transform (WT) and the independent component analysis (ICA), were utilized for feature extraction.  These algorithms used to reduces the dimensions of the data, which is an input to the optimized QNN for the purpose of performing the classification process after the feature extraction process. This research uses an optimized QNN, a form of feedforward neural network (FFNN), to recognize (EEG) signals. The Particle swarm optimization (PSO) algorithm was used to optimize the quantum neural network, which improved the training process of the system's performance. The optimized (QNN) provided us with somewhat faster and more realistic results. According to simulation results, the total classification for (ICA) is 82.4 percent, while the total classification for (WT) is 78.43 percent; from these results, using the ICA for feature extraction is better than using WT.
Co-Authors Ali Najim Abdullah Ali Talib Jawad Dalael Saad Abdul-Zahra