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Abstract:
In the analysis of data recorded by optical imaging from
intrinsic signals (measurement of changes of light reflectance from
cortical tissue) the removal of noise and artifacts such as blood
vessel patterns is a serious problem. Often bandpass filtering is
used, but the underlying assumption that a spatial frequency
exists, which separates the mapping component from other components
(especially the global signal), is questionable. Here we propose
alternative ways of processing optical imaging data, using blind
source separation techniques based on the spatial decorrelation of
the data. We first perfom benchmarks on artificial data in order to
select the way of processing, which is most robust with respect ot
sensor noise. We then apply it to recordings of optical imaging
experiments from macaque primary visual cortex. We show that our
BSS technique is able to extract ocular dominance and orientation
preference maps from single condition stacks, for data, where
standard postprocessing procedures fail. Artifacts, especially
blood vessel patterns, can often be completely removed from the
maps. In summary, our method for blind source separation using
extended spatial decorrelation is a superior technique for the
analysis of optical recording data.
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