Journal of Electrical Engineering and Technology

  • 제11권6호
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  • Pages.1812-1824
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  • 2016
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  • 1975-0102(pISSN)
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  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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Estimation of Brain Connectivity during Motor Imagery Tasks using Noise-Assisted Multivariate Empirical Mode Decomposition

  • Lee, Ki-Baek (Dept. of Electrical Engineering, Kwangwoon University) ;
  • Kim, Ko Keun (Intelligence Lab., Convergence Center at LG Electronics) ;
  • Song, Jaeseung (Dept. of Computer and Information Security, Sejong University) ;
  • Ryu, Jiwoo (Dept. of Computer Engineering, Kwangwoon University) ;
  • Kim, Youngjoo (Dept. of Computer Engineering, Kwangwoon University) ;
  • Park, Cheolsoo (Dept. of Computer Engineering, Kwangwoon University)
  • 투고 : 2015.05.27
  • 심사 : 2016.07.21
  • 발행 : 2016.11.01
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초록

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

키워드

과제정보

연구 과제번호 : The Development of oneM2M Conformance Testing Tool and QoS Technology

연구 과제 주관 기관 : National Research of Korea(NRF), Institute for Information & communications Technology Promotion(IITP)

참고문헌

  1. C. Park, C. C. Took, and D. P. Mandic, "Augmented complex common spatial patterns for classification of noncircular EEG from motor imagery tasks," IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 22, no. 1, pp. 1-10, Dec. 2013 https://doi.org/10.1109/TNSRE.2013.2294903
  2. C. Park, D. Looney, P. Kidmose, M.Ungstrup and D.P. Mandic "Time-Frequency Analysis of EEG Asymmetry using Bivariate Empirical Mode Decomposition" IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19, no. 4, pp. 366-373, 2011 https://doi.org/10.1109/TNSRE.2011.2116805
  3. K. Sivasankari and K. Thanushkodi, "An Improved EEG Signal Classification Using Neural Network with the Consequence of ICA and STFT," Journal of Electrical Engineering and Technology, vol. 9, no. 3, pp. 1060-1071, 2014 https://doi.org/10.5370/JEET.2014.9.3.1060
  4. X. Li, S. O, Y. Pan and K. Ang, "Connectivity pattern modeling of motor imagery EEG," in Proc. Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2013, pp, 16-19
  5. C. Park, D. Looney, N. Rehman, A. Ahrabian and D. P. Mandic, "Classification of motor imagery BCI using multivariate empirical mode decomposition," IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 21, no. 1, pp. 10-22, Jan. 2013 https://doi.org/10.1109/TNSRE.2012.2229296
  6. A. Solodkin, P. Hlustik, E. E. Chen and S. L. Small, "Fine modulation in network activation during motor execution and motor imagery," Cerebral Cortex, vol. 14, pp. 1246-55, Nov. 2004 https://doi.org/10.1093/cercor/bhh086
  7. C. W. J. Granger, "Investigating causal relations by econometric models and cross-spectral methods," Econometrica," vol. 37, no. 3, pp. 424-438, Aug. 1969 https://doi.org/10.2307/1912791
  8. A. N. Silchenko, I. Adamchica, N. Pawelczyka, C. Hauptmanna, M. Maaroufb, V. Sturmb and P. A. Tassa, "Data-driven approach to the estimation of connectivity and time delays in the coupling of interacting neuronal subsystems," Journal of Neuroscience Methods, vol. 191, no. 1, pp. 32-44, Aug. 2010 https://doi.org/10.1016/j.jneumeth.2010.06.004
  9. K. Sameshima and L. A. Baccal, "Using partial directed coherence to describe neuronal ensemble interactions," Journal of Neuroscience Methods, vol. 94, no. 1, pp. 93-103, Dec. 1999 https://doi.org/10.1016/S0165-0270(99)00128-4
  10. N. E. Huang, Z. Shen, S. R. Long, M. L. Wu, H. H. Shih, Z. Quanan, N. C. Yen, C. C. Tung and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society A vol. 454, pp. 903-95, Mar. 1998 https://doi.org/10.1098/rspa.1998.0193
  11. C. Park, M. Plank, J. Snider, S. Kim, H. Huang, S. Gepshtein, T. Coleman, H. Poizner, "EEG gamma band oscillations differentiate the planning of spatially directed movements of the arm versus eye: multivariate empirical mode decomposition analysis," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, pp. 1083-96, 2014 https://doi.org/10.1109/TNSRE.2014.2332450
  12. S. Lahmiri and M. Boukadoum, "A weighted biosignal denoising approach using empirical mode decomposition," Biomedical Engineering Letters, vol. 5, no. 2, pp. 131-139, 2015 https://doi.org/10.1007/s13534-015-0182-2
  13. D. Looney and D. P. Mandic, "Multi-scale image fusion using complex extensions of EMD," IEEE Transactions on Signal Processing," vol. 57 pp. 1626-30, 2009 https://doi.org/10.1109/TSP.2008.2011836
  14. N Rehman and D. P. Mandic, "Multivariate empirical mode decomposition," Proceedings of the Royal Society A, vol. 466, pp. 1291-302, 2010 https://doi.org/10.1098/rspa.2009.0502
  15. J. Rodrigues and A. Andrade, "Instantaneous Granger Causality with the Hilbert-Huang Transform," ISRN Signal Processing, pp. 1-9, 2013
  16. Z. Wu and N. E. Huang, "Ensemble empirical mode decomposition:A noise-assisted data analysis method," Advances in Adaptive Data Analysis, vol. 1, no. 1, 2009
  17. J. Cui J and W. Freeden, "Equidistribution on the sphere," Journal on Scientific Computing, vol. 18, pp. 595-609, 1997 https://doi.org/10.1137/S1064827595281344
  18. N. Rehman and D. P. Mandic, "Filter bank property of multivariate empirical mode decomposition," IEEE Transactions on Signal Processing, vol. 59, 2421-6, 2011 https://doi.org/10.1109/TSP.2011.2106779
  19. G. Rilling, P. Flandrin and P. Goncalves, "On empirical mode decomposition and its algorithms," Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP), vol. 3, pp. 8-11, 2003
  20. N. E. Huang, M. L. Wu, S. R. Long, S. S. P. Shen, W. Qu W, P. Gloersen and K. L. Fan, "A confidence limit for the empirical mode decomposition and Hilbert spectral analysis," Proceedings of the Royal Society A, vol. 459, pp. 2317-45, 2003 https://doi.org/10.1098/rspa.2003.1123
  21. L. A. Baccal, and K. Sameshima, "Partial directed coherence: a new concept in neural structure determination," Biological Cybernetics, vol. 84 pp. 463-74, 2001 https://doi.org/10.1007/PL00007990
  22. G. Schalk, D. McFarland, T. Hinterberger, N. Birbaumer and J. R. Wolpaw, "BCI2000: A general purpose brain-computer interface (BCI) system," IEEE Transactions on Biomedical Engineering, vol. 51, pp. 1034-43, 2004 https://doi.org/10.1109/TBME.2004.827072
  23. A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P. Ivanov, R. Mark, J. Mietus, G. Moody, C. K. Peng and H. Stanley, "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals Circulation," 101e215-e220
  24. M. Arvaneh, C. Guan, K. K. Ang and C. Quek, "Optimizing the channel selection and classification accuracy in EEG-Based BCI," IEEE Transactions on Biomedical Engineering, vol. 58, 1873, 2011
  25. H. Ramoser, J. Muller-Gerking and G. Pfurtscheller, "Optimal spatial filtering of single trial EEG during imagined hand movement," IEEE Transactions on Rehabilitation Engineering, vol. 8, pp. 441-6, 2000 https://doi.org/10.1109/86.895946
  26. J. Muller-Gerking, G. Pfurtscheller H. Flyvbjerg, "Designing optimal spatial filters for single-trial EEG classification in a movement task," Clinical Neuro-physiology, vol. 110, pp. 787-98, 1999
  27. K. Lugger, D. Flotzinger, A. Schlol, M. Pregenzer and G. Pfurtscheller, "Feature extraction for on-line EEG classification using principal components and linear discriminants," Medical & Biological Engineering & Computing, vol. 36, pp. 309-314, 1998 https://doi.org/10.1007/BF02522476
  28. R. Noori, A. R. Karbassi, A. Moghaddamnia, D. Han, M. H. Zokaei-Ashtiani, A. Farokhnia and M. G. Gousheh, "Assessment of input variables deter mination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction," Journal of Hydrology, vol. 401, no. 3-4, pp. 177-189, 2011 https://doi.org/10.1016/j.jhydrol.2011.02.021
  29. R. E. Bellman, "Dynamic Programming" North Chelmsford, MA: Courier Dover Publications
  30. H. Drucker, C. J. Burges, L. Kaufman, A. Smola and V. Vapnik, "Support Vector Regression Machines," Cambridge, MA: MIT Press
  31. S. Canu, Y. Grandvalet, V. Guigue and A. Rakotomamonjy, "SVM and kernel methods Matlab toolbox", Perception Systems et Information (Rouen, France: INSA de Rouen, 2005
  32. G. R. Muller-Putz, R. Scherer, C. Brunner, R. Leeb, and G. Pfurtscheller, "Better than random? a closer look on BCI results," International Journal of Bioelectromagnetism, vol. 10, no. 1, pp. 52-55, 2008
  33. M. Ahn and S. Jun, "Performance variation in motor imagery brain-computer interface: A brief review," Journal of Neuroscience Methods, vol. 243, pp. 103-110, 2015 https://doi.org/10.1016/j.jneumeth.2015.01.033
  34. J. A. Hanley and B. J. McNeil,, "A method of comparing the areas under receiver operating characteristic curves derived from the same cases," Radiology, vol. 148, pp. 839-43, 1983 https://doi.org/10.1148/radiology.148.3.6878708
  35. H. Yuan, A. Doud, A. Gururajan and B. He, "Cortical imaging of event- related (de)synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 16, pp. 425-31, 2008 https://doi.org/10.1109/TNSRE.2008.2003384
  36. Y. Chung, J. Kang and S. Kim, "Analysis of correlated EEG activity during motor imagery for brain computer interfaces," in Proceedings of Control, Automation and Systems (ICCAS), pp. 337-41, 2011
  37. M. Kaminski, M. Ding, W. A. Truccolo and S. Bressle, "Evaluating causal relations in neural systems : Granger causality, directed transfer function and statistical assessment of significance," Biological Cybernetics, vol. 85, pp. 145-57, 2011
  38. Y. Kim and C. Park, "Strong Uncorrelated Transform Applied to Spatially Distant Channel EEG Data," IEIE Transactions on Smart Processing and Computing vol. 4 , pp. 97-102, Apr. 2015 https://doi.org/10.5573/IEIESPC.2015.4.2.097
  39. S. Ge, M. Han and X. Hong, "A fully automatic ocular artifact removal from EEG based on fourth order tensor method," Biomedical Engineering Letters vol. 4, no. 1, pp. 55-63, Mar. 2014 https://doi.org/10.1007/s13534-014-0118-2

피인용 문헌

  1. Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals vol.16, pp.1, 2017, https://doi.org/10.1186/s12938-017-0397-9