Classification of EEG Signal by Using Optimized Quantum Neural Network

Dalael Saad Abdul-Zahra (Dept. of Medical Physics Hilla University College Babylon, Iraq)
Ali Talib Jawad (Dept. of Medical Instrumentation Technologies Engineering Hilla University College Babylon, Iraq)
Hassan Muwafaq Gheni (Computer Techniques Engineering Department, Al-Mustaqbal University college, Hillah 51001, Iraq)
Ali Najim Abdullah (Dept. of Medical Instrumentation Technologies Engineering Hilla University College Babylon, Iraq)

Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Vol 9, No 4: December 2021

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.