独立成分分析を用いた内因性光信号の光源ノイズ除去アルゴリズムと評価

DOI Web Site
  • 吉田 侑冬
    東北大学大学院情報科学研究科バイオモデリング論分野
  • 中川 大輝
    東北大学大学院情報科学研究科バイオモデリング論分野
  • 辛島 彰洋
    東北工業大学工学研究科電子工学専攻
  • 中尾 光之
    東北大学大学院情報科学研究科バイオモデリング論分野
  • 片山 統裕
    東北大学大学院情報科学研究科バイオモデリング論分野

書誌事項

タイトル別名
  • Performance Evaluation of Light Source Noise Reduction Algorithm Based on Independent Component Analysis for Optical Intrinsic Signal Data
  • ドクリツ セイブン ブンセキ オ モチイタ ナイインセイ ヒカリ シンゴウ ノ コウゲン ノイズ ジョキョ アルゴリズム ト ヒョウカ

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抄録

Intrinsic optical signal (OIS) imaging technique is widely used in neuroscience research because it permits high resolution brain mapping without introducing molecular probes into the brain. However, low signal-to-noise ratio (S/N) of OIS is a serious problem that has to be resolved. So far, many algorithms have been developed to improve S/N. However, most of them require repeated acquisition of stimulus-to-response data and are therefore not suitable for OIS that express spontaneous activity of the brain. To overcome this problem, we developed an independent component analysis (ICA)-based algorithm for reduction of light source noise from OIS. The algorithm is based on a model of mixing mechanism of light source noise. It automatically determines the number of independent components and finds the component corresponding to light source noise based on similarity of power spectral densities. The noise component is removed by projecting the measured OIS onto the subspace orthogonal to the subspace spanned by the estimated noise component. Although usability of the algorithm was demonstrated by applying to real OIS data, some parameters were not optimized and quantitative performance was not clarified. In this study, we evaluated the noise reduction ability of the system by conducting performance test using synthetic OIS data containing light source noise. First, we identified the optimal parameter value for binning processing, which is applied prior to the noise reduction algorithm, based on accuracy of estimation of noise component. Second, we showed that reduction of light source noise by 10-20 dB was achieved under optimal conditions. These results indicate superiority of the algorithm and suggest its usefulness in improving S/N of real OIS data expressing spontaneous activity of the brain.

収録刊行物

  • 生体医工学

    生体医工学 53 (6), 328-335, 2015

    公益社団法人 日本生体医工学会

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