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Preprocessing Pipeline for fNIRS Data in Children

  • Caterina PiazzaEmail author
  • Andrea Bacchetta
  • Alessandro Crippa
  • Maddalena Mauri
  • Silvia Grazioli
  • Gianluigi Reni
  • Maria Nobile
  • Anna Maria Bianchi
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 76)

Abstract

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique, largely used in paediatric research. However, there is not a standardized and widely accepted protocol for fNIRS data processing with potential effects on the reliability and replicability of the obtained results. The present study is within this framework aiming at the identification of an adequate pre-processing pipeline to be used for the analysis of children fNIRS datasets. The performance of five different motion correction techniques, based on the principal component analysis and on the wavelet filtering, was evaluated by analyzing fNIRS data recorded in 22 typically developing children (mean age 11.4 years). The results showed that the wavelet analysis combined with a moving average filter achieved the best performance, suggesting that this technique might become a gold-standard approach for motion artifacts correction in fNIRS children’s datasets.

Keywords

Functional Near-Infrared Spectroscopy Children Motion correction Principal component analysis Wavelet filtering 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Caterina Piazza
    • 1
    Email author
  • Andrea Bacchetta
    • 1
  • Alessandro Crippa
    • 1
  • Maddalena Mauri
    • 1
  • Silvia Grazioli
    • 1
  • Gianluigi Reni
    • 1
  • Maria Nobile
    • 1
  • Anna Maria Bianchi
    • 2
  1. 1.Scientific Institute, IRCCS E. MedeaBosisio PariniItaly
  2. 2.Department of Electronics Information and BioengineeringPolitecnico di MilanoMilanItaly

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