Reduction of light source noise from optical intrinsic signals of mouse neocortex by using independent component analysis

Yuto Yoshida, Daiki Nakagawa, Akihiro Karashima, Mitsuyuki Nakao, Norihiro Katayama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Because the optical intrinsic signal (OIS) of the brain is very weak, noise reduction is essential. Independent component analysis (ICA) is widely used for noise reduction. However, the applicability of ICA to the reduction of light source (LS) noise has not been discussed in detail. In addition, determining the proper number of independent components (ICs) for decomposition is very important to a reasonable classification of the ICs. In this study, we considered the applicability of ICA to LS noise reduction by modeling the impact of LS noise on OIS data. We propose a method for determining the number of ICs that uses the power spectral density of LS noise. To evaluate its usefulness, the method was applied to real OIS data of a mouse's cerebral cortex.

Original languageEnglish
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6277-6280
Number of pages4
ISBN (Electronic)9781424492718
DOIs
Publication statusPublished - 2015 Nov 4
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: 2015 Aug 252015 Aug 29

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2015-November
ISSN (Print)1557-170X

Other

Other37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/TerritoryItaly
CityMilan
Period15/8/2515/8/29

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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