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Feasibility Study of EEG-based Real-time Brain Activation Monitoring System

뇌파 기반 실시간 뇌활동 모니터링 시스템의 타당성 조사

  • Chae, Hui-Je (Department of Biomedical Engineering, Yonsei University) ;
  • Im, Chang-Hwan (Department of Biomedical Engineering, Yonsei University) ;
  • Lee, Seung-Hwan (Department of Neuropsychiatry, Inje University Ilsan-Paik Hospital)
  • 채희제 (연세대학교 의공학부) ;
  • 임창환 (연세대학교 의공학부) ;
  • 이승환 (인제대 일산백병원 신경정신과)
  • Published : 2007.04.30

Abstract

Spatiotemporal changes of brain rhythmic activity at a certain frequency have been usually monitored in real time using scalp potential maps of multi-channel electroencephalography(EEG) or magnetic field maps of magnetoencephalography(MEG). In the present study, we investigate if it is possible to implement a real-time brain activity monitoring system which can monitor spatiotemporal changes of cortical rhythmic activity on a subject's cortical surface, neither on a sensor plane nor on a standard brain model, with a high temporal resolution. In the suggested system, a frequency domain inverse operator is preliminarily constructed, considering the individual subject's anatomical information, noise level, and sensor configurations. Spectral current power at each cortical vertex is then calculated for the Fourier transforms of successive sections of continuous data, when a single frequency or particular frequency band is given. An offline study which perfectly simulated the suggested system demonstrates that cortical rhythmic source changes can be monitored at the cortical level with a maximal delay time of about 200 ms, when 18 channel EEG data are analyzed under Pentium4 3.4GHz environment. Two sets of artifact-free, eye closed, resting EEG data acquired from a dementia patient and a normal male subject were used to show the feasibility of the suggested system. Factors influencing the computational delay are investigated and possible applications of the system are discussed as well.

Keywords

References

  1. O. Jensen and s. Vanni, 'A new method to identify multiple sources of oscillatory sctivity from magnetoencephalographic data,' NeuroImage, vol. 15, pp. 568-574, 2002 https://doi.org/10.1006/nimg.2001.1020
  2. S. Salenius, M. Kajola, W.L. Thompson, S. Kosslyn, and R. Hari, 'Reactivity of magnetic parieto-occipital alpha rhythm during visual imagery,' Electroencephalogr. Clin. Neurophysiol., vol. 95, pp. 453-462, 1995 https://doi.org/10.1016/0013-4694(95)00155-7
  3. R. Salmelin and R. Hari, 'Spatiotempora1 characteristics of sensorimotor neuromagnetic rhythms related to thumb movement,' Neuroscience, vol. 60, pp. 537-550, 1994 https://doi.org/10.1016/0306-4522(94)90263-1
  4. T. Gruber, M.M. Muller, A. Keil, and T. Elbert, 'Selective visual-spatial attention alters induced gamma band responses in the human,' EEG. Clin. Neurophysiol., vol. 110, pp. 2074-2085, 1999 https://doi.org/10.1016/S1388-2457(99)00176-5
  5. J. Kaiser, B. Ripper, N. Birbaumer, and W. Lutzenberger, 'Dynamics of gamma-band activity in human magnetoencephalogran: during auditory pattern working memory,' NeuroImage, vol. 20, pp, 816-827, 2003 https://doi.org/10.1016/S1053-8119(03)00350-1
  6. W.H. Miltner, C. Braun, M. Arnold, H. Witte, and E. Taub, 'Coherence of gamma-band EEG activity as a basis for associative learning,' Nature, vol. 397, pp. 434-436, 1999 https://doi.org/10.1038/17126
  7. J.S. Kwon, B.F. O'Donnell, G.V. Wallenstein, R. W. Greene, Y. Hirayasu, P.G. Nestor, M.E. Hasselmo, G.F. Potts, M.E. Shenton, and R.W. McCarley, 'Gamma frequency-range abnormalities to auditory stimulation in schizophrenia,' Arch. Gen. Psychiatry, vol. 56,pp.1001-1005, 1999 https://doi.org/10.1001/archpsyc.56.11.1001
  8. D. Osipova, J. Ahveninen, O. Jensen, A. Ylikoski, and E. Pekkonen, 'Altered generation of spontaneous oscillations in Alzheimer's disease,' NeuroImage, vol. 27, pp. 835-841, 2005 https://doi.org/10.1016/j.neuroimage.2005.05.011
  9. A. Kubler, B. Kotchoubey, J. Kaiser, J.R.Wolpaw, and N. Birbaumer, 'Brain-computer communication: Unlocking the locked,' Psycholog. Bull., vol. 127, pp. 358-375, 2001 https://doi.org/10.1037/0033-2909.127.3.358
  10. M. Congedo, J.F. Lubar, and D. Joffe, 'Low-Resolution electromagnetic tomography neurofeedback,' IEEE Trans. Neural. Syst. Rehabil. Eng., vol. 12, pp. 387-397, 2004
  11. R. Salmelin, A. Schnitzler, F. Schmitz, and H.J. Freund, 'Single word reading in developmental stutterers and fluent speakers,' Brain, vol. 123, pp. 1184-1202, 2000 https://doi.org/10.1093/brain/123.6.1184
  12. J. Gross, J. Kujala, M. Hamalainen, L. Timmermann, A. Schnitzler, and R. Salmelin, 'Dynamic imaging of coherent sources: Studying neural interactions in the human brain,' in Proc. Natl. Acad. Sci. USA, vol. 98, pp. 694-699, 2001
  13. O. Jensen and S. Vanni, 'A new method to identify multiple sources of oscillatory activity from magnetoencephalographic data,' NeuroImage, vol. 15, pp, 568-574, 2002 https://doi.org/10.1006/nimg.2001.1020
  14. F.H. Lin, T. Witzel, M.S. Hamalainen, A.M. Dale, J.W. Belliveau, and S.M. Stufflebeam, 'Spectral spatiotemporal imaging of cortical oscillations and interations in the human brain,' NeuroImage, vol. 23, pp.582-595, 2004 https://doi.org/10.1016/j.neuroimage.2004.04.027
  15. M. Congedo, J.F. Lubar, and D. Joffe, 'Low-resolution electromagnetic tomography neurofeedback,' IEEE Trans. Neural Syst. Rehab. Eng., vol. 12, pp. 387-397, 2004 https://doi.org/10.1109/TNSRE.2004.840492
  16. M. Congedo, 'Subspace projection filters for real-time brain electromagnetic imaging,' IEEE Trans. Biomed. Eng., vol. 53, pp.1624-1634, 2006 https://doi.org/10.1109/TBME.2006.878055
  17. R.D. Pascual-Marqui, Low Resolution Electromagnetic Tomography(LORETA), downloadable at http://www.unizh.ch/keyinst/NewLORETA/LORETA01.htm
  18. B. He, T. Musha, Y. Okamoto, S. Homma, Y. Nakajima and T. Sato, 'Electric dipole tracing in the brain by means of the boundary element method and its accuracy,' IEEE Trans. Biomed. Eng., vol. 34,pp.406-414, 1987 https://doi.org/10.1109/TBME.1987.326056
  19. M.S. Hamalainen and J. Sarvas, 'Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data,' IEEE Trans. Biomed. Eng., vol. 36, pp. 165-171, 1989 https://doi.org/10.1109/10.16463
  20. J. Haueisen, C. Ramon, M. Eiselt, H. Brauer and H. Nowak, 'Influence of tissue resistivities on neuromagnetic fields and electric potentials studied with a finite element model of the head,' IEEE Trans. Biomed. Eng., vol. 44, pp. 727-735, 1997 https://doi.org/10.1109/10.605429
  21. T.F. Oostendorp, J. Delbeke and D.F. Stegeman, 'The conductivity of the human skull: results of in vivo and in vitro measurements,' IEEE Trans. Biomed. Eng., vol. 47, pp. 14871492, 2000 https://doi.org/10.1109/TBME.2000.880100
  22. C.H. Im, BioEST A free software to reconstruct EEG/MEG source distribution, information can be found at http://bem.yonsei.ac.kr
  23. A.M. Dale and M.I. Sereno, 'Improved localization of cortical activity by combining EEG and MEG with MRI surface reconstruction: a linear approach,' J. Cogn. Neurosci., vol. 5, pp.162-176, 1993 https://doi.org/10.1162/jocn.1993.5.2.162
  24. W.E. Kincses, C. Braun, S. Kaiser, and T. Elbert, 'Modeling extended sources of event-related potentials using anatomical and physiological constraints,' Hum. Brain Mapp., vol. 8, pp.182-193, 1999 https://doi.org/10.1002/(SICI)1097-0193(1999)8:4<182::AID-HBM3>3.0.CO;2-M
  25. A.M. Dale, A.K. Liu, B.R. Fischl, R.L. Buckner, J.W. Belliveau, J.D. Lewine, and E. Halgren, 'Dynamic Statistical Parametric Mapping: Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity,' Neuron, vol. 26, pp.55-67, 2000 https://doi.org/10.1016/S0896-6273(00)81138-1
  26. C. Babiloni, 'Linear inverse source estimate of combined EEG and MEG data related to voluntary movements,' Hum. Brain Mapp., vol. 14, pp.197-209, 2001 https://doi.org/10.1002/hbm.1052
  27. S. Baillet, J.J. Riera, G. Marin, J.F. Mangin, J. Aubert, and L. Gamero, 'Evaluation of inverse methods and head models for EEG source localization using a human skull phantom,' Phys. Med. Biol., vol. 46. pp.77-96, 2001 https://doi.org/10.1088/0031-9155/46/1/306
  28. A.K. Liu, J.W. Belliveau, and A.M. Dale, 'Spatiotemporal imaging of human brain activity using functional MRI constrained magneto encephalography data: Monte Carlo simulations,' in Proc. Natl. Acad. Sci. USA, vol. 95, pp. 8945-8950, 1998
  29. A.M. Dale, B. Fischl, and M.I. Sereno, 'Cortical surface-based analysis I. segmentation and surface reconstruction,' Neuroimage, vol. 9, pp.179-194, 1999 https://doi.org/10.1006/nimg.1998.0395
  30. A.K. Liu, A.M. Dale, and J.W. Belliveau, 'Monte Carlo simulation studies of EEG and MEG localization accuracy,' Hum. Brain Mapp., vol. 16, pp.47-62, 2002 https://doi.org/10.1002/hbm.10024
  31. R. Salmelin and R. Hari, 'Characterization of spontaneous MEG rhythms in healthy adults,' Electroencephalogr. Clin. Neurophysiol., vol. 91, pp. 237-248, 1994 https://doi.org/10.1016/0013-4694(94)90187-2
  32. S. Vanni, A. Revonsuo, and R. Hari, 'Modulation of the parietooccipital alpha rhythm during object detection,' J. Neurosci., vol. 17, pp. 7141-7147, 1997 https://doi.org/10.1523/JNEUROSCI.17-18-07141.1997
  33. B. Kamousi, A.N. Amini, and B. He, 'Classification of motor imagery by means of cortical current density estimation and von neumann entropy for brain-computer interface applications,' J. Neural Eng., in press, 2007
  34. B. Kamousi, Z. Liu, and B. He, 'Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis,' IEEE Trans. Neural. Syst. Rehabil. Eng., vol. 13, pp. 166-171, 2005 https://doi.org/10.1109/TNSRE.2005.847386