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Partial Least Squares-discriminant Analysis for the Prediction of Hemodynamic Changes Using Near Infrared Spectroscopy

  • Seo, Youngwook (Department of Biomedical Engineering, Korea University) ;
  • Lee, Seungduk (Department of Biomicrosystem Engineering, Korea University) ;
  • Koh, Dalkwon (Department of Biomedical Engineering, Korea University) ;
  • Kim, Beop-Min (Department of Biomedical Engineering, Korea University)
  • 투고 : 2011.10.12
  • 심사 : 2011.12.26
  • 발행 : 2012.03.25

초록

Using continuous wave near-infrared spectroscopy, we measured time-resolved concentration changes of oxy-hemoglobin and deoxy-hemoglobin from the primary motor cortex following finger tapping tasks. These data were processed using partial least squares-discriminant analysis (PLS-DA) to develop a prediction model for a brain-computer interface. The tasks were composed of a series of finger tapping for 15 sec and relaxation for 45 sec. The location of the motor cortex was confirmed by the anti-phasic behavior of the oxy- and deoxy-hemoglobin changes. The results were compared with those obtained using the hidden Markov model (HMM) which has been known to produce the best prediction model. Our data imply that PLS-DA makes better judgments in determining the onset of the events than HMM.

키워드

참고문헌

  1. D. A. Boas, T. Gaudette, G. Strangman, X. Cheng, J. J. Marota, and J. B. Mandeville, "The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics," Neuroimage 13, 76-90 (2001).
  2. F. F. Jobsis, "Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters," Science 198, 1264-1267 (1977). https://doi.org/10.1126/science.929199
  3. T. Muehlemann, D. Haensse, and M. Wolf, "Wireless miniaturized in-vivo near infrared imaging," Opt. Express 16, 10323-10330 (2008). https://doi.org/10.1364/OE.16.010323
  4. T. Muehlemann, N. Reefmann, B. Wechsler, M. Wolf, and L. Gygax, "In vivo functional near-infrared spectroscopy measures mood-modulated cerebral responses to a positive emotional stimulus in sheep," Neuroimage 54, 1625-1633 (2011). https://doi.org/10.1016/j.neuroimage.2010.08.079
  5. S. Shimada, "Modulation of motor area activity by the outcome for a player during observation of a baseball game," PLoS One 4, e8034 (2009). https://doi.org/10.1371/journal.pone.0008034
  6. K. Tai and T. Chau, "Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface," J. Neuroeng. Rehabil. 6, 39 (2009). https://doi.org/10.1186/1743-0003-6-39
  7. J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. Schalk, E. Donchin, L. A. Quatrano, C. J. Robinson, and T. M. Vaughan, "Brain-computer interface technology: a review of the first international meeting," IEEE Trans. Rehabil. Eng. 8, 164-173 (2000). https://doi.org/10.1109/TRE.2000.847807
  8. J. J. Daly and J. R. Wolpaw, "Brain-computer interfaces in neurological rehabilitation," Lancet Neurol. 7, 1032-1043 (2008). https://doi.org/10.1016/S1474-4422(08)70223-0
  9. F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche, and B. Arnaldi, "A review of classification algorithms for EEG-based brain-computer interfaces," J. Neural Eng. 4, R1-R13 (2007). https://doi.org/10.1088/1741-2560/4/2/R01
  10. P. Brunner, A. L. Ritaccio, J. F. Emrich, H. Bischof, and G. Schalk, "A brain-computer interface using event-related potentials (Erps) and electrocorticographic signals (Ecog) in humans," Epilepsia 50, 389-389 (2009).
  11. N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, "Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI)," IEEE Trans. Biomed. Eng. 51, 966-970 (2004). https://doi.org/10.1109/TBME.2004.827063
  12. R. Sitaram, A. Caria, R. Veit, T. Gaber, G. Rota, A. Kuebler, and N. Birbaumer, "FMRI brain-computer interface: a tool for neuroscientific research and treatment," Comput. Intell. Neurosci. 2007, 25487 (2007).
  13. R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, "Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface," Neuroimage 34, 1416-1427 (2007). https://doi.org/10.1016/j.neuroimage.2006.11.005
  14. S. Coyle, T. Ward, C. Markham, and G. McDarby, "On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces," Physiol. Meas. 25, 815-822 (2004). https://doi.org/10.1088/0967-3334/25/4/003
  15. S. M. Coyle, T. E. Ward, and C. M. Markham, "Braincomputer interface using a simplified functional near-infrared spectroscopy system," J. Neural Eng. 4, 219-226 (2007). https://doi.org/10.1088/1741-2560/4/3/007
  16. N. Roche-Labarbe, B. Zaaimi, P. Berquin, A. Nehlig, R. Grebe, and F. Wallois, "NIRS-measured oxy- and deoxyhemoglobin changes associated with EEG spike-and-wave discharges in children," Epilepsia 49, 1871-1880 (2008). https://doi.org/10.1111/j.1528-1167.2008.01711.x
  17. S. D. Power, T. H. Falk, and T. Chau, "Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy," J. Neural Eng. 7, 026002 (2010). https://doi.org/10.1088/1741-2560/7/2/026002
  18. Z. Ghahramani, "An introduction to hidden Markov models and Bayesian networks," Int. J. Pattern Recogn. 15, 9-42 (2001). https://doi.org/10.1142/S0218001401000836
  19. L. R. Rabiner, "A tutorial on hidden Markov-models and selected applications in speech recognition," Proc. IEEE 77, 257-286 (1989). https://doi.org/10.1109/5.18626
  20. S. Luu and T. Chau, "Decoding subjective preference from single-trial near-infrared spectroscopy signals," J. Neural Eng. 6, 016003 (2009). https://doi.org/10.1088/1741-2560/6/1/016003
  21. L. Stahle and S. Wold, "Partial least squares analysis with cross-validation for the two-class problem: a Monte Carlo study," J. Chemometr. 1, 185-196 (1987). https://doi.org/10.1002/cem.1180010306
  22. A. R. McIntosh, F. L. Bookstein, J. V. Haxby, and C. L. Grady, "Spatial pattern analysis of functional brain images using partial least squares," Neuroimage 3, 143-157 (1996). https://doi.org/10.1006/nimg.1996.0016
  23. A. R. McIntosh, W. Chau, and A. B. Protzner, "Spatiotemporal analysis of event-related fMRI data using partial least squares," Neuroimage 23, 764-775 (2004). https://doi.org/10.1016/j.neuroimage.2004.05.018
  24. L. Holper, M. Biallas, and M. Wolf, "Task complexity relates to activation of cortical motor areas during uni- and bimanual performance: a functional NIRS study," Neuroimage 46, 1105-1113 (2009). https://doi.org/10.1016/j.neuroimage.2009.03.027
  25. R. W. Homan, J. Herman, and P. Purdy, "Cerebral location of international 10-20 system electrode placement," Electroencephalogr. Clin. Neurophysiol. 66, 376-382 (1987). https://doi.org/10.1016/0013-4694(87)90206-9
  26. G. Strangman, D. A. Boas, and J. P. Sutton, "Non-invasive neuroimaging using near-infrared light," Biol. Psychiat. 52, 679-693 (2002). https://doi.org/10.1016/S0006-3223(02)01550-0
  27. A. Krishnan, L. J. Williams, A. R. McIntosh, and H. Abdi, "Partial least squares (PLS) methods for neuroimaging: a tutorial and review," Neuroimage 56, 455-475 (2011). https://doi.org/10.1016/j.neuroimage.2010.07.034
  28. M. A. Franceschini, D. K. Joseph, T. J. Huppert, S. G. Diamond, and D. A. Boas, "Diffuse optical imaging of the whole head," J. Biomed. Opt. 11, 054007 (2006). https://doi.org/10.1117/1.2363365
  29. M. A. Franceschini and D. A. Boas, "Noninvasive measurement of neuronal activity with near-infrared optical imaging," Neuroimage 21, 372-386 (2004). https://doi.org/10.1016/j.neuroimage.2003.09.040
  30. S. Wold, M. Sjostrom, and L. Eriksson, "PLS-regression: a basic tool of chemometrics," Chemometr. Intell. Lab. 58, 109-130 (2001). https://doi.org/10.1016/S0169-7439(01)00155-1
  31. I. Nambu, R. Osu, M. A. Sato, S. Ando, M. Kawato, and E. Naito, "Single-trial reconstruction of finger-pinch forces from human motor-cortical activation measured by near-infrared spectroscopy (NIRS)," Neuroimage 47, 628-637 (2009). https://doi.org/10.1016/j.neuroimage.2009.04.050

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