Signal processing of functional NIRS data acquired during overt speaking

Abstract

Abstract. Functional near-infrared spectroscopy (fNIRS) offers an advantage over traditional functional imaging methods [such as functional magnetic resonance imaging (fMRI)] by allowing participants to move and speak relatively freely. However, neuroimaging while actively speaking has proven to be particularly challenging due to the systemic artifacts that tend to be located in the critical brain areas. To overcome these limitations and enhance the utility of fNIRS, we describe methods for investigating cortical activity during spoken language tasks through refinement of deoxyhemoglobin (deoxyHb) signals with principal component analysis (PCA) spatial filtering to remove global components. We studied overt picture naming and compared oxyhemoglobin (oxyHb) and deoxyHb signals with and without global component removal using general linear model approaches. Activity in Broca's region and supplementary motor cortex was observed only when the filter was applied to the deoxyHb signal and was shown to be spatially comparable to fMRI data acquired using a similar task and to meta-analysis data. oxyHb signals did not yield expected activity in Broca's region with or without global component removal. This study demonstrates the utility of a PCA spatial filter on the deoxyHb signal in revealing neural activity related to a spoken language task and extends applications of fNIRS to natural and ecologically valid conditions. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Real-Time Eye-to-Eye Contact Is Associated With Cross-Brain Neural Coupling in Angular Gyrus

“…Recent techniques that employ spatial filtering and short channel separation to remove these artifacts have been developed (Gagnon et al, 2014;Goodwin et al, 2014;Zhang et al, 2016Zhang et al, , 2017. Here, when the spatial filtering technique was employed (Zhang et al, 2016(Zhang et al, , 2017 we found that the deOxyHb signals in the ROI analysis showed a significant difference between groups, and the OxyHb signals revealed a similar trend. Although eventtriggered average results indicated localized concordance of Oxyand deOxyHb signals associated with neural processing, the additional variance in the OxyHb signal (seen in the error bars in Figure 5) may have contributed to the lack of a significant difference, although a consistent trend is observed between the two signals.…”

“…Due to the optical methods of fNIRS, signals may contain systemic effects that originate from cardiovascular rather than neural sources (Tachtsidis and Scholkmann, 2016). Recent techniques that employ spatial filtering and short channel separation to remove these artifacts have been developed (Gagnon et al, 2014;Goodwin et al, 2014;Zhang et al, 2016Zhang et al, , 2017. Here, when the spatial filtering technique was employed (Zhang et al, 2016(Zhang et al, , 2017 we found that the deOxyHb signals in the ROI analysis showed a significant difference between groups, and the OxyHb signals revealed a similar trend.…”

“…Functional NIRS signal acquisition, optode localization, and signal processing, including global mean removal, were similar to methods described previously (Noah et al, 2015(Noah et al, , 2017Zhang et al, 2016Zhang et al, , 2017Piva et al, 2017;Dravida et al, 2018;Hirsch et al, 2018) and are summarized below. To assure that all participants provided recordable hemodynamic signals using fNIRS prior to participation in this experiment, subjects who demonstrated a significant fNIRS signal (p < 0.05) in the left motor cortex for both OxyHb and deOxyHb signals were eligible to participate in the present study.…”

“…Baseline drift was removed using wavelet detrending provided in NIRS-SPM (Ye et al, 2009). Global components attributable to blood pressure and other systemic effects (Tachtsidis and Scholkmann, 2016) were removed using a principal component analysis (PCA) spatial, global-mean filter (Zhang et al, 2016(Zhang et al, , 2017 prior to general linear model (GLM) analysis. Comparisons between conditions were based on GLM procedures using the NIRS-SPM software package.…”

Optimization of wavelet coherence analysis as a measure of neural synchrony during hyperscanning using functional near-infrared spectroscopy

“…21 These global components were removed using a principle component analysis spatial filter. 22,23 This technique exploits advantages of distributed optode coverage by spatial filtering to distinguish signal components originating from local sources (assumed to be specific to neural events under investigation) from global components assumed to be systemic factors that originate from non-neural sources. Findings are similar for both spatially filtered OxyHb and deOxyHb signals, as illustrated in Fig.…”