Classification of EEG Signals Using Quantum Neural Network and Cubic Spline

• Autorzy: Abdul-Zahra Raheem M. , AbdulRazzaq Hussein E.

Identyfikatory

DOI

10.1515/eletel-2016-0055

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

signals; ERP signals; cubic spline; neural networks; quantum neural network

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2016
• Tom: Vol. 62, No. 4
• Strony: 401--408
• Opis fizyczny: Bibliogr. 14 poz., ryst., tab., wykr.

Twórcy

autor

Abdul-Zahra Raheem M.

• University of Babylon, College of Engineering, Electrical Engineering Department, Iraq

autor

AbdulRazzaq Hussein E.

• University of Babylon, College of Engineering, Electrical Engineering Department, Iraq

Bibliografia

• [1] Saeid Sanei and Jonathon Chambers, “EEG signal processing”, Cardiff University, England, 2007.
• [2] Terence W. Picton and others, “The recording and analysis of event-related potentials”, Elsevier Science, Germany, Vol. (10), 1995.
• [3] David Friedman and Ray Johnson, “Event-Related Potential (ERP) Studies of Memory Encoding and Retrieval: A Selective Review”, Microscopy Research and Technique, New York, 2000.
• [4] Sky McKinley and Megan Levine, “Cubic Spline Interpolation,” CiteSeer, February, 1999.
• [5] CJC Kruger, “Constrained Cubic Spline Interpolation for Chemical Engineering Applications,” M.S.C. Thesis, 2002.
• [6] S. Fredenhagen, H. J. Oberle, & G. Opfer, “On the Construction of Optimal Monotone Cubic Spline Interpolations,” Journal of Theory, ELSEVIER, February, p.p.182-201, 1999.
• [7] Professor J. Zhang, “Approximation by Spline Functions”, lecture 6, Dep. of Computer Science, University of Kentucky Lexington, KY 402060046, November 1, 2010.
• [8] Maojun Cao, Fuhua Shang, “Quantum Neural Networks with application in adjusting PID parameters”, IEEE, 2009.
• [9] Dianbao Mu and others, “Learning Algorithm and Application of Quantum Neural Networks with Quantum Weights,” International Journal of Computer Theory and Engineering, Vol. (5), 2013.
• [10] Gopathy Purushothaman and Nicolaos B. Karayiannis, “Quantum Neural Networks (QNN’s): Inherently Fuzzy Feedforward Neural Networks”, IEEE, VOL. (8), 1997.
• [11] Shicai Yu and Ning Ma, “Quantum Neural Network and its Application in Vehicle Classification”, IEEE, 2008.
• [12] Daqi Zhu and Rushi Wu, “A Multi-layer Quantum Neural Networks Recognition System for Handwritten Digital Recognition”, IEEE, 2007.
• [13] Ajith Abraham, “Handbook of Measuring System Design”, Oklahoma State University, Stillwater, OK, USA, 2005.
• [14] Zachary A. Keirn, “Alternative modes of communication between man and machine,” M.S.C Thesis, Purdue University, 1988.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.