Noise removal in NIRS

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Noise removal methods in NIRS can be divided into 4 categories:

  1. reducing noise based on its temporal characteristics: The instrument noise is usually in the high frequency band and thus can be removed by band pass filtering. Band pass filtering can also remove low frequency drift. A real-time version of band pass filtering is exponential moving average (EMA, Utsugi 2007).
  2. reducing noise based on its spatial characteristics: motion related noise is assumed to be more spatially spread. The “common” component of the signal across multiple channels (e.g. using PCA) can be treated as noise. (Zhang 2005; Wilcox 2005)
  3. reducing noise based on its effect on the correlation between oxy- and deoxy-Hb: motion noise will make the correlation between oxy- and deoxy-Hb, which is typically negative, less negative. (Cui 2010) check out
  4. measuring noise independently and subtracting it from the signal. (Zhang 2007, 2009)

Band pass filtering or moving average performs pretty well in reducing non-spike like noise and this method is a standard component in my data analysis. For large spike-like motion artifact, correlation based method works fairly well (even in real-time settings). Of course, for offline analysis, one can also remove these large spikes manually (or semi-automatically).

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