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Minicourse EDEP-LP17

Software tools for assessing and enhancing the quality of fNIRS experimental measurements

Luca Pollonini

with Samuel Montero-Hernandez

About this course

This mini-course describes and demonstrates software tools for the assessment and improvement of the quality of fNIRS signals before, during and after data collection. These tools are commonly based on a quantitative method for estimating optode-scalp coupling and the presence of movement artifacts through a combination of time- and frequency-domain measures of the physiological, systemic pulsation present in raw fNIRS signals. As such, this approach is applicable to datasets acquired with any fNIRS device, and we reduced it to practice by implementing in two MATLAB applications freely available to the fNIRS community:

Learning Outcomes

  • Observe and learn first-hand about the typical features of high- vs. low-quality fNIRS signals.
  • Understand how fNIRS signals can be assessed automatically and independently of specific experimental paradigms, optical layouts, or devices.

Course Plan

Level: Introductory

Pre-Requisites: None
Course Duration: 2 hours

Delivery Plan

30 min: presentation about the typical features of high- vs. low-quality fNIRS signals, and how systemic oscillations otherwise deemed undesirable for cortical functional analysis can be used as robust indicators of fNIRS signal quality.
90 min: hands-on demonstration of the use and utility of PHOEBE and QT-NIRS.

Why enrol on this course?

Functional near-infrared spectroscopy (fNIRS) is an ever-growing optical technique that has seen a proliferation of new instruments addressing the research interests of scientists and clinicians alike. However, the assessment of fNIRS data quality and the comparison between data collected with different instruments remain challenging due to the lack of a standard method that defines and quantifies the signal-to-noise ratio (SNR) of an fNIRS signal. In addition, movement artifacts may temporarily compromise the quality of otherwise clean fNIRS signals and, therefore, may require the rejection of experimental trials with unrecoverable signals. This mini-course seeks to bring together new and seasoned fNIRS investigators to promote and further develop practical, easily interpretable methods for assessing fNIRS data quality.