Classification of EEG Signals Using Quantum Neural Network and Cubic Spline

Mariam Abdul-Zahra Raheem and Ehab AbdulRazzaq Hussein

Abstract—

The main aim of this paper is to propose Cubic Spline-Quantum Neural Network (CS-QNN) model for analysis and classification of Electroencephalogram (EEG) signals. Experimental data used here were taken from seven different electrodes. The work has been done in three stages, normalization of the signals, extracting the features by Cubic Spline Technique(CST) and classification using Quantum Neural Network (QNN. The simulation results showed that five types of EEG signals were classified with an average accuracy for seven electrodes that is 94.3% when training 70% of the features while with an average accuracy of 92.84% when training 50% of the features.

I. INTRODUCTION
The biomedical engineering interested dramatically in the automatic classification of Electroencephalogram (EEG)signals. Because the biomedical signals, inherently unstable and randomly change over time depending on the change and mental
health conditions and situations of tension for the same person, and one of these signals is brain signal that varies according to the psychological state of the person himself and changed depending on the circumstances, all of this has paid great attention to the analysis of brain signals. The EEG is the registration of electrical activity on the scalp. Current flow due to firing of nerve cells in the brain results in a voltage wiggle that measured as EEG [1]. Measuring the brain s response to a stimulus is called event-related potential (ERP). The stimulus can be motor, sensory, or cognitive naturally. Human ERPs are usually recorded from electrodes placed on the human scalp.