Minicourse-22

Synopsis: This mini-course describes and demonstrates software tools for assessment and improvement of 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 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:

(1) PHOEBE (Placing Headgear Optodes Efficiently Before Experimentation), a graphical tool that interfaces to fNIRS instruments via Lab Streaming Layer (LSL) and displays the scalp coupling of optodes in real time to facilitate the optimal placement of a fNIRS headgear and to monitor the quality of signals during data collection, akin to electrical impedance used in electroencephalography, and

(2) QT-NIRS, a tool for post-hoc, at-a-glance assessment of data quality of an entire experiment at the individual or group level that automatically identifies and intuitively displaying time period and optical channels with low quality data (due to artifacts or other contaminants) and optionally, it restores compromised fNIRS signals to improve further analysis. interfaces with other tools for enabling the correction or discarding of low-quality signals (at the channel or trial level) as part of the processing pipeline.

Demonstrations of both software tools will be delivered using different devices and datasets. This mini-course welcomes new fNIRS researchers from different backgrounds seeking to strengthen their data collection and assessment skills, and it encourages all users to contribute ideas towards establishing a consensus on fNIRS signal quality control.

Rationale: 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.

Course structure: At the outset, attendants will learn about the typical features of high- vs. low-quality fNIRS signals, such as coupled vs. uncoupled optodes, and movement artifacts (estimated course time: ~10 mins). Then, they will learn how systemic oscillations otherwise deemed undesirable for cortical functional analysis can be used as robust indicators of fNIRS signal quality (estimated course time: ~20 mins). Finally, ample time (estimated course time: ~60 mins) will be dedicated to demonstrating the use and utility of PHOEBE and QT-NIRS. First, we will show how to interface PHOEBE to wearable fNIRS instruments (as available on site) for live assessment of data quality and optimization of optodes placement. Subsequently, we will evaluate individual- and group-level fNIRS scans collected with different fNIRS devices (NIRx, Shimadzu, Obelab, Techen) with QT-NIRS, and interface with Homer and AnalyzIR.

Learning objectives: Attendants will observe and learn first-hand about the typical features of high- vs. low-quality fNIRS signals, and understand how they can be assessed automatically and independently of specific experimental paradigm, optical layouts or devices.

Software Download: Attendants may optionally bring a laptop with MATLAB and openly available code for PHOEBE at this link and QT-NIRS at this link.