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

  • Authors:

  • Randall L. Barbour;Harry L. Graber;Yong Xu;Yaling Pei;Glenn R. Wylie;Gerald T. Voelbel;John Deluca;Andrei V. Medvedev

  • Affiliations:

  • Department of Pathology, SUNY Downstate Medical Center, Brooklyn, 11203 and NIRx Medical Technologies, Glen Head, 11545;Department of Pathology, SUNY Downstate Medical Center, Brooklyn, 11203 and NIRx Medical Technologies, Glen Head, 11545;Department of Pathology, SUNY Downstate Medical Center, Brooklyn, 11203 and NIRx Medical Technologies, Glen Head, 11545;NIRx Medical Technologies, Glen Head, 11545;Kessler Foundation Research Center, W. Orange, 07101;Kessler Foundation Research Center, W. Orange, 07101;Kessler Foundation Research Center, W. Orange, 07101;Center for Functional and Molecular Imaging, Georgetown University Medical Center, Washington, 20057

  • Venue:
  • FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
  • Year:
  • 2009

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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.