Evaluating Quantum Neural Network filtered motor imagery brain-computer interface using multiple classification techniques

Neurocomputing

Volumn 170, Issue , 2015, Pages 161-167

  Gandhi, Vaibhav ^a   Prasad, Girijesh ^b   Coyle, Damien ^b   Behera, Laxmidhar ^c   McGinnity, Thomas Martin ^d  

^a MIDDLESEX UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF ULSTER   (United Kingdom)

^c INDIAN INSTITUTE OF TECHNOLOGY KANPUR   (India)

^d NOTTINGHAM TRENT UNIVERSITY   (United Kingdom)

Author keywords

Brain computer interface (BCI); EEG; Filtering; Recurrent quantum neural network (RQNN)

Indexed keywords

BIOMEDICAL SIGNAL PROCESSING; CLASSIFICATION (OF INFORMATION); COMPUTER NETWORKS; ELECTROENCEPHALOGRAPHY; FILTRATION; IMAGE CLASSIFICATION; INFORMATION FILTERING; RECURRENT NEURAL NETWORKS; SCHRODINGER EQUATION;

CLASSIFICATION ACCURACY; MULTIPLE CLASSIFICATION; MULTIPLE CLASSIFIERS; PARAMETRIC SENSITIVITY; QUANTUM NEURAL NETWORKS; SCHRODINGER WAVE EQUATIONS; SENSORIMOTOR CORTEX; STATISTICAL BEHAVIOR;

BRAIN COMPUTER INTERFACE;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN COMPUTER INTERFACE; CLASSIFICATION; DATA PROCESSING; DIGITAL FILTERING; ELECTROENCEPHALOGRAM; INFORMATION PROCESSING; INTERMETHOD COMPARISON; MACHINE LEARNING; MODEL; MOTOR COORDINATION; MOTOR IMAGERY; NOISE REDUCTION; PRIORITY JOURNAL; RECURRENT QUANTUM NEURAL NETWORK; SENSITIVITY ANALYSIS; SIGNAL NOISE RATIO; STATISTICAL ANALYSIS;

EID: 84940614757     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.12.114     Document Type: Article

References (20)

1. Delorme A., Sejnowski T., Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage 2007, 34:1443-1449.
2. Nunez P.L., Srinivasan R. Electric Fields of the Brain: The Neurophysics of EEG 2006, Oxford University Press, USA.
3. Romero S., Mañanas A.M., Barbanoj J M. Ocular reduction in EEG signals based on adaptive filtering, regression and blind source separation. Ann. Biomed. Eng. 2009, 37:176-191.
4. Pfurtscheller G., Lopes da Silva H F. Handbook of electroencephalography and clinical neurophysiology-revised series. Event-Related Desynchronization 1999, 6.
5. Gandhi V. Brain-Computer Interfacing for Assistive Robotics: Electroencephalograms, Recurrent Quantum Neural Networks, and User-Centric Graphical Interfaces 2014, Academic Press.
6. Gandhi V., Prasad G., Coyle D., Behera L., McGinnity T.M. Quantum neural network based EEG filtering for a brain-computer interface. IEEE Trans. Neural Netw. Learn. Syst. 2014, 25:278-288.
7. Shankar R. Principles of Quantum Mechanics 1994, Springer.
8. Planck M. Zur Theorie Des Gesetzes Der Energieverteilung Im Normalspectrum 1900, Barth.
9. L. Behera, S. Bharat, S. Gaurav and A. Manish, A recurrent network model with neurons activated by schroedinger wave equation and its application to stochastic filtering, in: Proceedings of the 9th International Conference on High-Performance Computing, Workshop on Soft Computing, 2002.
10. Behera L., Kar I., Elitzur A.C. A Recurrent Quantum Neural Network model to describe eye tracking of moving targets. Found. Phys. Lett. 2005, 18:357-370.
11. V. Gandhi and T.M. McGinnity, Quantum neural network based surface EMG signal filtering for control of robotic hand, in: Proceedings of the IEEE International Joint Conference on Neural Networks, 2013.
12. Ivancevic V.G. Adaptive-wave alternative for the Black-Scholes option pricing model. Cognit. Comput. 2010, 2:17-30.
13. Hjorth B. EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 1970, 29:306-310.
14. Behera L., Kar I., Elitzur A.C. Recurrent Quantum Neural Network and its applications. Emerg. Phys. Conscious. 2006, 327-350.
15. Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 1964, 36:1627-1639.
16. Hargittai S. Savitzky-Golay least-squares polynomial filters in ECG signal processing. IEEE Comput. Cardiol. 2005, 763-766.
17. Hassanpour H. A time-frequency approach for noise reduction. Digit. Signal Process. 2008, 18:728-738.
18. Zehtabian A., Zehtabian B. A novel noise reduction method based on subspace division. J. Comput. Eng. 2009, 1:55-61.
19. Leeb R., Brunner C., Muller-Putz G.R., Schlogl A., Pfurtscheller G. BCI competition 2008-graz data set b 2008, Graz University of Technology, Austria.
20. Schlögl A., Kronegg J., Huggins J.E., Mason S.G. "Evaluation criteria for BCI research," in Towards Brain-Computer Interfacing 2007, 327-490. MIT Press, Cambridge, MA, USA. G. Dornhege, J. Millán, T. Hinterberger, D. McFarland, K.-R. Müller (Eds.).