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Systems and Strategies for Accessing the Information Content of fNIRS Imaging in Support of Noninvasive BCI Applications

  • Randall L. Barbour
  • Harry L. Graber
  • Yong Xu
  • Yaling Pei
  • Glenn R. Wylie
  • Gerald T. Voelbel
  • John DeLuca
  • Andrei V. Medvedev
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)

Abstract

An essential component for a practical noninvasive brain-computer interface (BCI) system is data recording technology that can access the information-processing activity of the brain with high fidelity and throughput. Functional near-infrared spectroscopic (fNIRS) imaging is a methodology that shows promise in meeting this need, having a demonstrated sensitivity to both the slow hemodynamic response that follows neuroactivation and to the lower amplitude fast optical response that is considered a direct correlate of neuroactivation. In this report we summarize the technology integration strategy we have developed that permits detection of both signal types with a single measuring platform, and present results that document the ability to detect these data types transcranially in response to two different visual paradigms. Also emphasized is the effectiveness of different data analysis approaches that serve to isolate signals of interest. The findings support the practical utility of NIRS-based imaging methods for development of BCI applications.

Keywords

Diffuse Optical Tomography fNIRS imaging fast signal combinatorial Hb States Neuroactivation Visual Stimulus NIRS Technology 

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References

  1. 1.
    Barbour, R.L., Graber, H.L., Aronson, R., Lubowsky, J.: Model for 3-D optical imaging of tissue. In: Int. Geosci. and Remote Sensing Symp (IGARSS), pp. 1395–1399. IEEE, Piscataway (1990)CrossRefGoogle Scholar
  2. 2.
    Aronson, R., Barbour, R.L., Lubowsky, J., Graber, H.L.: Application of transport theory to infra-red medical imaging. In: Operator Theory: Advances and Applications, vol. 51, pp. 64–75. Birkhäuser Verlag, Basel (1991)Google Scholar
  3. 3.
    Barbour, R.L., Graber, H.L., Aronson, R., Lubowsky, J.: Imaging of subsurface regions of random media by remote sensing. In: SPIE Proceedings, vol. 1431, pp. 192–203. SPIE Press, Bellingham (1991)Google Scholar
  4. 4.
    Barbour, R.L., Graber, H.L., Chang, J., Wang, Y., Aronson, R.: A perturbation approach for optical diffusion tomography using continuous-wave and time-resolved data. In: SPIE Institute of Medical Optical Tomography: Functional Imaging and Monitoring, pp. 87–120 (1993)Google Scholar
  5. 5.
    Graber, H.L., Chang, J., Aronson, R., Barbour, R.L.: A perturbation model for imaging in dense scattering media: derivation and evaluation of imaging operators. In: SPIE Institute of Medical Optical Tomography: Functional Imaging and Monitoring, pp. 121–143 (1993)Google Scholar
  6. 6.
    Schmitz, C.H., Löcker, M., Lasker, J.M., Hielscher, A.H., Barbour, R.L.: Instrumentation for fast functional optical tomography. Rev. Sci. Instrum. 73, 429–439 (2002)CrossRefGoogle Scholar
  7. 7.
    Schmitz, C.H., Klemer, D.P., Hardin, R.E., Katz, M.S., Pei, Y., Graber, H.L., Levin, M.B., Levina, R.D., Franco, N.A., Solomon, W.B., Barbour, R.L.: Design and implementation of dynamic near-infrared optical tomographic imaging instrumentation for simultaneous dual-breast measurements. Applied Optics 44, 2140–2153 (2005)CrossRefPubMedGoogle Scholar
  8. 8.
    Schmitz, C.H., Graber, H.L., Pei, Y., Farber, M.B., Stewart, M., Levina, R.D., Levin, M.B., Xu, Y., Barbour, R.L.: Dynamic studies of small animals with a four-color DOT imager. Rev. Sci. Instrum. 76, 094302 (2005)Google Scholar
  9. 9.
    Koizumi, H., Yamamoto, T., Maki, A., Yamashita, Y., Sato, H., Kawaguchi, H., Ichikawa, N.: Optical topography: practical problems and new applications. Applied Optics 42, 3054–3062 (2003)CrossRefPubMedGoogle Scholar
  10. 10.
    Matsuo, K., Kato, T., Taneichi, K., Matsumoto, A., Ohtani, T., Hamamoto, T., Yamasue, H., Sakano, Y., Sasaki, T., Sadamatsu, M., Iwanami, A., Asukai, N., Kato, N.: Activation of the prefrontal cortex to trauma–related stimuli measured by near–infrared spectroscopy in posttraumatic stress disorder due to terrorism. Psychophysiology 40, 492–500 (2003)CrossRefPubMedGoogle Scholar
  11. 11.
    Clark, C.R., Moores, K.A., Lewis, A., Weber, D.L., Fitzgibbon, S., Greenblatt, R., Brown, G., Taylor, J.: Cortical network dynamics during verbal working memory function. Int. J. Physophysiol. 42, 161–176 (2001)Google Scholar
  12. 12.
    Pei, Y., Graber, H.L., Barbour, R.L.: Influence of systematic errors in reference states on image quality and on stability of derived information for DC optical imaging. Applied Optics 40, 5755–5769 (2001)CrossRefPubMedGoogle Scholar
  13. 13.
    Pei, Y., Graber, H.L., Barbour, R.L.: Normalized-constraint algorithm for minimizing inter-parameter crosstalk in DC optical tomography. Optics Express 9, 97–109 (2001)CrossRefPubMedGoogle Scholar
  14. 14.
    Graber, H.L., Pei, Y., Barbour, R.L.: Imaging of spatiotemporal coincident states by DC optical tomography. IEEE Trans. Med. Imag. 21, 852–866 (2002)CrossRefGoogle Scholar
  15. 15.
    Barbour, R.L., Graber, H.L., Xu, Y., Pei, Y., Aronson, R.: Strategies for imaging diffusing media. Transport Theory Stat. Phys. 33, 361–371 (2004)CrossRefGoogle Scholar
  16. 16.
    Graber, H.L., Xu, Y., Pei, Y., Barbour, R.L.: Spatial deconvolution technique to improve the accuracy of reconstructed three-dimensional diffuse optical tomographic images. Applied Optics 44, 941–953 (2005)CrossRefPubMedGoogle Scholar
  17. 17.
    Xu, Y., Graber, H.L., Pei, Y., Barbour, R.L.: Improved accuracy of reconstructed diffuse optical tomographic images by means of spatial deconvolution: two-dimensional quantitative characterization. Applied Optics 44, 2115–2139 (2005)CrossRefPubMedGoogle Scholar
  18. 18.
    Xu, Y., Pei, Y., Graber, H.L., Barbour, R.L.: Image quality improvement via spatial deconvolution in optical tomography: Time-series imaging. J. Biomed. Optics 10, 51701 (2005)CrossRefGoogle Scholar
  19. 19.
    Xu, Y., Graber, H.L., Barbour, R.L.: An image correction algorithm for functional 3D DOT brain imaging. Applied Optics 46, 1693–1704 (2007)CrossRefPubMedGoogle Scholar
  20. 20.
    Graber, H.L., Xu, Y., Barbour, R.L.: An image correction scheme applied to functional DOT scattering images. Applied Optics 46, 1705–1716 (2007)CrossRefPubMedGoogle Scholar
  21. 21.
    Stone, J.V.: Independent Component Analysis: A Tutorial Introduction. MIT Press, Cambridge (2004)Google Scholar
  22. 22.
    Stetter, M.: Exploration of Cortical Function. Kluwer Academic Publishers, Dordrecht (2002)CrossRefGoogle Scholar
  23. 23.
    Barbour, R.L., Graber, H.L., Pei, Y., Schmitz, C.H., Di Martino, A., Castellanos, F.X.: Site-Specific Monitoring of Cerebral Vascular Hemodynamics with Dynamic Optical Tomography. Poster No. TH 252 at: Human Brain Mapping (2004)Google Scholar
  24. 24.
    Laboratory of Computer and Information Science: Adaptive Informatics Research Centre, http://www.cis.hut.fi/projects/ica/fastica
  25. 25.
    Pei, Y., Wang, Z., Xu, Y., Graber, H.L., Monteiro, R., Barbour, R.L.: NAVI: A problem solving environment (PSE) for NIRS data analysis. In: Poster No. 114 at: Fifth Inter-Institute Workshop on Optical Diagnostic Imaging from Bench to Bedside at the National Institutes of Health (2006)Google Scholar
  26. 26.
    Garavan, H., Ross, T.J., Stein, E.A.: Right hemispheric dominance of inhibitory control: an event-related functional MRI study. Proc. Natl. Acad. Sci. USA 96, 8301–8306 (1999)CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Ward, B.D.: Simultaneous Inference for FMRI DATA, http://afni.nimh.nih.gov/pub/dist/doc/manual/AlphaSim.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Randall L. Barbour
    • 1
    • 2
  • Harry L. Graber
    • 1
    • 2
  • Yong Xu
    • 1
    • 2
  • Yaling Pei
    • 2
  • Glenn R. Wylie
    • 3
  • Gerald T. Voelbel
    • 3
  • John DeLuca
    • 3
  • Andrei V. Medvedev
    • 4
  1. 1.Department of PathologySUNY Downstate Medical CenterBrooklynUSA
  2. 2.NIRx Medical TechnologiesGlen HeadUSA
  3. 3.Kessler Foundation Research CenterW. OrangeUSA
  4. 4.Center for Functional and Molecular ImagingGeorgetown University Medical CenterWashingtonUSA

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