fNIRS: Non-stationary preprocessing methods

Dmitry Patashov*, Yakir Menahem, Guy Gurevitch, Yoshinari Kameda, Dmitry Goldstein, Michal Balberg


研究成果: Article査読

2 被引用数 (Scopus)


In this paper we present algorithms for preprocessing of functional Near Infrared Spectroscopy (fNIRS) data. We propose a statistical method that provides an automatic identification of noisy channels and a non-stationary filtering procedure for both detrending and removal of high frequency contamination sources. A recently published Cumulative Curve Fitting Approximation (CCFA) algorithm was used for the filtration of the signals to reduce distortion effects due to the non-stationarity of the fNIRS data. The output was compared to Discrete Cosine Transform (DCT) based filtering, followed by Low Pass Filtering (LPF) and to Band Pass Filtering (BPF) methods. The results demonstrate that CCFA based filtering can produce a greater Signal to Noise Ratio (SNR) improvement in comparison to the commonly/conventionally used methods.

ジャーナルBiomedical Signal Processing and Control
出版ステータスPublished - 2023 1月

ASJC Scopus subject areas

  • 信号処理
  • 生体医工学
  • 健康情報学


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