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The NIRS Analysis Package: Noise Reduction and Statistical Inference

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  • Tomer Fekete
  • Denis Rubin
  • Joshua M Carlson
  • Lilianne R Mujica-Parodi

Abstract

Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS.

Suggested Citation

  • Tomer Fekete & Denis Rubin & Joshua M Carlson & Lilianne R Mujica-Parodi, 2011. "The NIRS Analysis Package: Noise Reduction and Statistical Inference," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-12, September.
    • Handle: RePEc:plo:pone00:0024322
    DOI: 10.1371/journal.pone.0024322
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