Review of recent progress toward a fiberless, whole-scalp diffuse optical tomography system

Zhao, Hubin; Cooper, Robert J.

doi:10.1117/1.nph.5.1.011012

Cited by 90 publications

(83 citation statements)

References 45 publications

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“…The technique has found widespread use both in the research and clinical field despite its low penetration depth and spatial resolution, as it provides good portability, safety, and ecological validity at low-cost and is therefore well-suited for both experimental and real-life settings Yücel et al, 2017). Similar to EEG, recent advances in fNIRS instrumentation have led to an increasing number of wearable, light weight, and fiberless systems (Scholkmann et al, 2014;von Lühmann et al, 2015;Zhao and Cooper, 2017) and wearable hybrid EEG-fNIRS systems (Safaie et al, 2013;von Lühmann et al, 2017;Kassab et al, 2018) that help translate BCI research from laboratory environments into real world applications.…”

Section: Introductionmentioning

confidence: 99%

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Lühmann

Ortega-Martínez

Boas

et al. 2020

Front. Hum. Neurosci.

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Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing-on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI.

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Section: Introductionmentioning

confidence: 99%

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Lühmann

Ortega-Martínez

Boas

et al. 2020

Front. Hum. Neurosci.

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“…It uses 3 wavelengths (735 nm, 810 nm and 850 nm) [54]. The OBELAB company developed NIRSIT system for prefrontal cortical sampling [55]. It is probably the first fNIRS system that can provide high spatial resolution (millimeter-level) while also providing high temporal resolution (125 ms/8 Hz).…”

Section: Introductionmentioning

confidence: 99%

Optics Based Label-Free Techniques and Applications in Brain Monitoring

et al. 2020

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Functional near-infrared spectroscopy (fNIRS) has been utilized already around three decades for monitoring the brain, in particular, oxygenation changes in the cerebral cortex. In addition, other optical techniques are currently developed for in vivo imaging and in the near future can be potentially used more in human brain research. This paper reviews the most common label-free optical technologies exploited in brain monitoring and their current and potential clinical applications. Label-free tissue monitoring techniques do not require the addition of dyes or molecular contrast agents. The following optical techniques are considered: fNIRS, diffuse correlations spectroscopy (DCS), photoacoustic imaging (PAI) and optical coherence tomography (OCT). Furthermore, wearable optical brain monitoring with the most common applications is discussed.

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“…It is unlikely that fiber-based HD imaging will translate to broad applications as the more costly and bulky probes greatly undermine the fNIRS benefits of portability and flexibility. Instead, efforts focusing on developing wearable, fiberless systems have strived recently for higher spatial sampling density [18]. For instance, a recent modular DOT system offers the promise of combining high-density measurements with wearability [19] (Figure 2.d–f), and we envision that similar systems will be developed in the upcoming years to reconcile the needs of portability and spatial mapping accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Wearable fNIRS is the next advance that will transform the technology, enabling studies of brain activity associated with natural behaviors in ecologically valid settings and dramatically reducing the cost of fNIRS systems [18]. Taking full advantage of the portability of fNIRS will dramatically increase the spectrum of applications.…”

Section: Introductionmentioning

confidence: 99%

Functional Near Infrared Spectroscopy: Enabling routine functional brain imaging

Yücel

Selb

Huppert

et al. 2017

Current Opinion in Biomedical Engineering

167

134

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Functional Near-Infrared Spectroscopy (fNIRS) maps human brain function by measuring and imaging local changes in hemoglobin concentrations in the brain that arise from the modulation of cerebral blood flow and oxygen metabolism by neural activity. Since its advent over 20 years ago, researchers have exploited and continuously advanced the ability of near infrared light to penetrate through the scalp and skull in order to non-invasively monitor changes in cerebral hemoglobin concentrations that reflect brain activity. We review recent advances in signal processing and hardware that significantly improve the capabilities of fNIRS by reducing the impact of confounding signals to improve statistical robustness of the brain signals and by enhancing the density, spatial coverage, and wearability of measuring devices respectively. We then summarize the application areas that are experiencing rapid growth as fNIRS begins to enable routine functional brain imaging.

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Review of recent progress toward a fiberless, whole-scalp diffuse optical tomography system

Cited by 90 publications

References 45 publications

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Optics Based Label-Free Techniques and Applications in Brain Monitoring

Functional Near Infrared Spectroscopy: Enabling routine functional brain imaging

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