대한의용생체공학회:의공학회지 (Journal of Biomedical Engineering Research)

  • 제29권4호
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  • Pages.329-336
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  • 2008
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  • 1229-0807(pISSN)
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  • 2288-9396(eISSN)
대한의용생체공학회 (The Korean Society of Medical and Biological Engineering)
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뇌 신호원의 시계열 추출 및 인과성 분석에 있어서 ICA 기반 접근법과 MUSIC 기반 접근법의 성능 비교 및 문제점 진단

Comparison of ICA-based and MUSIC-based Approaches Used for the Extraction of Source Time Series and Causality Analysis

  • Jung, Young-Jin (Department of Biomedical Engineering, Yonsei University) ;
  • Kim, Do-Won (Department of Biomedical Engineering, Yonsei University) ;
  • Lee, Jin-Young (Department of Biomedical Engineering, Yonsei University) ;
  • Im, Chang-Hwan (Department of Biomedical Engineering, Yonsei University)
  • 발행 : 2008.08.30
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초록

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.

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참고문헌

  1. L. Ding and B. He, "Spatio-temporal EEG source localization using a three-dimensional subspace FINE approach in a realistic geometry inhomogeneous head model," IEEE Trans. Biomed. Eng., vol.53, no.9, pp.1732-1739, 2006 https://doi.org/10.1109/TBME.2006.878118
  2. L. Ding, C. Wilke, B. Xu, X. Xu, W. van Drongelen, M. Kohrman and B. He, "EEG source imaging: correlating source locations and extents with electrocorticography and surgical resections in epilepsy patients," J. Clin. Neurophysiol, vol.24, no.2, pp.130-136, 2007 https://doi.org/10.1097/WNP.0b013e318038fd52
  3. L. Ding, G. A. Worrell, T. D. Lagerlund and B. He, "Ictal source analysis: localization and imaging of causal interactions in humans," Neuroimage, vol.34, no.2, pp.575-586, 2007 https://doi.org/10.1016/j.neuroimage.2006.09.042
  4. C. W. Hesse and C. J. James, "Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure source scalp topographies," Med. Biol. Eng. Comput., vol.45, no. 10, pp.909-916, 2007 https://doi.org/10.1007/s11517-006-0103-8
  5. C. I. Hung, P. S. Wang, B. W. Soong, S. Teng. J, C. Hsieh and Y. T. Wu, "Blind source separation of concurrent disease-related patterns from EEG in Creutzfeldt-Jakob disease for assisting early diagnosis," Ann. Biomed. Eng., vol.35, no.12, pp.2168-2179, 2007 https://doi.org/10.1007/s10439-007-9381-z
  6. J. Iriarte, E. Urrestarazu, J. Artieda, M. Valencia, P. Levan, C. Viteri and M. Alegre, "Independent component analysis in the study of focal seizures," J. Clin. Neurophysiol, vol.23, no.6, pp.551-558,2006 https://doi.org/10.1097/01.wnp.0000236579.08698.23
  7. Jausovec, N. and K. Jausovec. "Spatiotemporal brain activity related to intelligence: a low resolution brain electromagnetic tomography study." Brain. Res. Cogn. Brain Res., vol. 16 no.2 pp.267-72, 2003 https://doi.org/10.1016/S0926-6410(02)00282-3
  8. K. Kobayashi, T. Akiyama, T. Nakahori, H. Yoshinaga and J. Gotman, "Systematic source estimation of spikes by a combination of independent component analysis and RAP-MUSIC. I: Principles and simulation study," Clin. Neurophysiol., vol.113, no.5, pp.713-724, 2002 https://doi.org/10.1016/S1388-2457(02)00046-9
  9. K. Kobayashi, T. Akiyama, T. Nakahori, H. Yoshinaga and J. Gotman, "Systematic source estimation of spikes by a combination of independent component analysis and RAP-MUSIC. II: Preliminary clinical application," Clin. Neuro-physiol., vol. 113, no.5, pp.725-734, 2002
  10. T. B. Liu, L. M. Tang and M. Wu, "[Study on an epilepsy EEG processing system based on independent component analysis (ICA)]," Zhongguo YiLiao QiXie Za Zhi, vol.31, no. 1, pp.26-29, 2007
  11. C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli and R. Grave de Peralta, "EEG source imaging," Clin. Neurophysiol., vol.115, no. 10, pp.2195-2222, 2004 https://doi.org/10.1016/j.clinph.2004.06.001
  12. J. C. Mosher, P. S. Lewis and R. M. Leahy, "Multiple dipole modeling and localization from spatio-temporal MEG data," IEEE Trans. Biomed. Eng., vol.39, no.6, pp.54l-557, 1992
  13. X. Wang, J. Meng and T. Qiu, "Research on chaotic behavior of epilepsy electroencephalogram of children based on independent component analysis algorithm," Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, vol.24, no.4, pp.835-841, 2007
  14. A. K. Liu, J. W. Belliveau and A. M. Dale, "Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations," Proc. Natl. Acad. Sci. USA, vol.95, no.15, pp.8945- 8950, 1998 https://doi.org/10.1073/pnas.95.15.8945
  15. M. J. Kaminski and K. J. Blinowska, "A new method of the description of the information flow in the brain structures," Biol. Cybern., vol.65, no.3, pp.203-210, 1991 https://doi.org/10.1007/BF00198091
  16. Granger, C. W. J., "Investigating causal relations by econometric models and cross-spectral methods." Econometrica vol.37, pp.424-438 1969 https://doi.org/10.2307/1912791
  17. Y. J. Lee, Y. S. Zhu, Y. H. Xu, M. F. Shen, H. X. Zhang and N. V. Thakor, "Detection of non-linearity in the EEG of schizophrenic patients," Clin. Neurophysiol., vol.112, no.7, pp.1288-1294, 2001 https://doi.org/10.1016/S1388-2457(01)00544-2
  18. A. J. Bell and T. J. Sejnowski, "An information-maximization approach to blind separation and blind deconvolution," Neural Comput, vol.7, no.6, pp.1129-1159, 1995 https://doi.org/10.1162/neco.1995.7.6.1129
  19. T. Schreiber and A. Schmitz, "Surrogate time series," Physica D-Nonlin. Phenom., vol. 142, no.3-4, pp.346-382, 2000 https://doi.org/10.1016/S0167-2789(00)00043-9