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Classification of EEG Signals Using Quantum Neural Network and Cubic Spline

Abdul-Zahra Raheem M., AbdulRazzaq Hussein E.

International Journal of Electronics and Telecommunications

2016

Vol. 62, No. 4

401--408

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.