Classification of EEG Signals Using Quantum Neural Network and Cubic Spline

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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.

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International Journal of Electronics and Telecommunications

The Journal of Committee of Electronics and Telecommunications of Polish Academy of Sciences

Journal Information

CiteScore 2016: 0.72

SCImago Journal Rank (SJR) 2016: 0.248
Source Normalized Impact per Paper (SNIP) 2016: 0.542


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