Signal quality index: an algorithm for quantitative assessment of functional near infrared spectroscopy signal quality

Sappia, María Sofía; Hakimi, Naser; Colier, Willy N.J.M.; Horschig, Jörn M.

doi:10.1364/boe.409317

Cited by 32 publications

(39 citation statements)

References 32 publications

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“…We suggest using the optical signal strength as an exclusion criterion when performing applied research (e.g., robot control by a BCI and clinical studies) and reporting the level of raw signal strength in future publications. Performing screening prior to a neuroscientific study has been proposed, 70 – 72 and metrics related to signal quality, such as the scalp coupling index, 73 the light-tissue coupling index, 74 or the signal quality index, 75 could be adapted. Second, MWs showed a significant effect on

-values, with all subjects with low MW amplitudes exhibiting high

-values, thereby confirming the relevant literature.…”

Section: Discussionmentioning

confidence: 99%

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

et al. 2022

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. Significance: Functional near-infrared spectroscopy (fNIRS) enables the measurement of brain activity noninvasively. Optical neuroimaging with fNIRS has been shown to be reproducible on the group level and hence is an excellent research tool, but the reproducibility on the single-subject level is still insufficient, challenging the use for clinical applications. Aim: We investigated the effect of short-channel regression (SCR) as an approach to obtain fNIRS measurements with higher reproducibility on a single-subject level. SCR simultaneously considers contributions from long- and short-separation channels and removes confounding physiological changes through the regression of the short-separation channel information. Approach: We performed a test-retest study with a hand grasping task in 15 healthy subjects using a wearable fNIRS device, optoHIVE. Relevant brain regions were localized with transcranial magnetic stimulation to ensure correct placement of the optodes. Reproducibility was assessed by intraclass correlation, correlation analysis, mixed effects modeling, and classification accuracy of the hand grasping task. Further, we characterized the influence of SCR on reproducibility. Results: We found a high reproducibility of fNIRS measurements on a single-subject level ( and correlation ). SCR increased the reproducibility from 0.64 to 0.81 ( ) but did not affect classification (85% overall accuracy). Significant intersubject variability in the reproducibility was observed and was explained by Mayer wave oscillations and low raw signal strength. The raw signal-to-noise ratio (threshold at 40 dB) allowed for distinguishing between persons with weak and strong activations. Conclusions: We report, for the first time, that fNIRS measurements are reproducible on a single-subject level using our optoHIVE fNIRS system and that SCR improves reproducibility. In addition, we give a benchmark to easily assess the ability of a subject to elicit sufficiently strong hemodynamic responses. With these insights, we pave the way for the reliable use of fNIRS neuroimaging in single subjects for neuroscientific research and clinical applications.

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-values, with all subjects with low MW amplitudes exhibiting high

-values, thereby confirming the relevant literature.…”

Section: Discussionmentioning

confidence: 99%

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

et al. 2022

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“…Currently, a number of algorithms based on morphological characteristics of the fNIRS signal have been proposed for signal quality assessment: (i) Scalp Coupling Index (SCI [11]), (ii) placing headgear optodes efficiently before experimentation (PHOEBE [12]), and (iii) signal quality index (SQI [13]). The SCI and PHOEBE are algorithms that binarily assign signals to "good" or "bad" categories based on the presence of the cardiac component in the signal.…”

Section: Introductionmentioning

confidence: 99%

“…Concerning signals' quality estimation, a machine learning version of the SQI (MLSQI [18]) has been developed based on the training dataset described in Sappia et al [13]. However, the training dataset was collected from only 14 participants and labeled by individuals working at the company that produces the fNIRS recording device used.…”

Section: Introductionmentioning

confidence: 99%

fNIRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control

et al. 2021

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Despite technological advancements in functional Near Infra-Red Spectroscopy (fNIRS) and a rise in the application of the fNIRS in neuroscience experimental designs, the processing of fNIRS data remains characterized by a high number of heterogeneous approaches, implicating the scientific reproducibility and interpretability of the results. For example, a manual inspection is still necessary to assess the quality and subsequent retention of collected fNIRS signals for analysis. Machine Learning (ML) approaches are well-positioned to provide a unique contribution to fNIRS data processing by automating and standardizing methodological approaches for quality control, where ML models can produce objective and reproducible results. However, any successful ML application is grounded in a high-quality dataset of labeled training data, and unfortunately, no such dataset is currently available for fNIRS signals. In this work, we introduce fNIRS-QC, a platform designed for the crowd-sourced creation of a quality control fNIRS dataset. In particular, we (a) composed a dataset of 4385 fNIRS signals; (b) created a web interface to allow multiple users to manually label the signal quality of 510 10 s fNIRS segments. Finally, (c) a subset of the labeled dataset is used to develop a proof-of-concept ML model to automatically assess the quality of fNIRS signals. The developed ML models can serve as a more objective and efficient quality control check that minimizes error from manual inspection and the need for expertise with signal quality control.

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“…To assess NIRS signal quality, the presence of a strong cardiac component has been often used as the main indicator of a reliable sensor-scalp coupling [105][106][107][108]. A reliable sensor-scalp coupling guarantees that NIR light travels through both intra-and extracranial layers.…”

Section: Signal Quality Assessmentmentioning

confidence: 99%

Neuroprotection of the Perinatal Brain by Early Information of Cerebral Oxygenation and Perfusion Patterns

Costa

Hakimi

Bel

2021

IJMS

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Abnormal patterns of cerebral perfusion/oxygenation are associated with neuronal damage. In preterm neonates, hypoxemia, hypo-/hypercapnia and lack of cerebral autoregulation are related to peri-intraventricular hemorrhages and white matter injury. Reperfusion damage after perinatal hypoxic ischemia in term neonates seems related with cerebral hyperoxygenation. Since biological tissue is transparent for near infrared (NIR) light, NIR-spectroscopy (NIRS) is a noninvasive bedside tool to monitor brain oxygenation and perfusion. This review focuses on early assessment and guiding abnormal cerebral oxygenation/perfusion patterns to possibly reduce brain injury. In term infants, early patterns of brain oxygenation helps to decide whether or not therapy (hypothermia) and add-on therapies should be considered. Further NIRS-related technical advances such as the use of (functional) NIRS allowing simultaneous estimation and integrating of heart rate, respiration rate and monitoring cerebral autoregulation will be discussed.

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Signal quality index: an algorithm for quantitative assessment of functional near infrared spectroscopy signal quality

Cited by 32 publications

References 32 publications

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

fNIRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control

Neuroprotection of the Perinatal Brain by Early Information of Cerebral Oxygenation and Perfusion Patterns

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