2013
DOI: 10.1515/bmt-2013-4430
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Physiological Noise Removal from fNIRS Signals

Abstract: In the present study we report on the reduction of physiological rhythms in hemodynamic ((de)oxy-Hb) signals recorded with functional near-infrared spectroscopy (fNIRS). We investigated the use of three different signal processing approaches (spatial filtering, adaptive filtering and transfer function (TF) models) to reduce the influence of respiratory and blood pressure rhythms on the hemodynamic responses. The results show that all three methods are promising for reducing the influence from oxy-Hb signals, b… Show more

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Cited by 13 publications
(16 citation statements)
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“…Additionally, trials containing motion artifacts were excluded for calculating task‐related changes and topographic distributions. A common average reference (CAR) spatial filtering approach (Bauernfeind et al, ) was used to reduce global influences from the [oxy‐Hb] signals. The idea behind the application of CAR is the fact that the global interfering signals influence all channels and hence can be reduced by calculating the mean of all channels and subtracting it from each single channel and for each time point.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Additionally, trials containing motion artifacts were excluded for calculating task‐related changes and topographic distributions. A common average reference (CAR) spatial filtering approach (Bauernfeind et al, ) was used to reduce global influences from the [oxy‐Hb] signals. The idea behind the application of CAR is the fact that the global interfering signals influence all channels and hence can be reduced by calculating the mean of all channels and subtracting it from each single channel and for each time point.…”
Section: Methodsmentioning
confidence: 99%
“…These signals, including quasi-periodic physiological rhythms like heart pulsation, breathing cycles, or low-frequency oscillations of the blood pressure but also task-evoked changes (systemic global influences; for details, see Bauernfeind et al, 2014), can mask the cerebral activation patterns. Therefore, also an effective reduction of these influences is required (Bauernfeind, B€ ock, Wriessnegger, & M€ uller-Putz, 2013;Bauernfeind et al, 2014;Boas, Dale, & Franceschini, 2004;Heinzel et al, 2013;Kirlilna et al, 2013). In recent years, fNIRS has been used primarily for cognitive, visual, or motor neurosciences (for an overview, see Bauernfeind, 2012;Ferrari & Quaresima, 2012).…”
mentioning
confidence: 99%
“…Third, because we employed a cross-sectional design, we could not examine the longitudinal causal relationships between occupational burnout and cortical activity. Fourth, we cannot totally exclude the influences of physiological noises on NIRS signals as we did not use signal processing approaches such as common average reference, adaptive filtering or transfer function models42. Fifth, since most of the study participants were female, we did not investigate the effects of menstrual cycle on brain function, as it has been shown that estrogen and progesterone have a modulatory effect on brain activity43.…”
Section: Limitationsmentioning
confidence: 99%
“…Second, the number of repetitions of the stimulus should be large enough to obtain statistical significance for the difference between stimulus conditions. Moreover, several recent studies have shown certain problematic aspects with NIRS measurements, such as systemic artifacts 78,79 or the differential pathlength factors (DPFs). 80 DPF can be measured using time-or frequency-domain instrumentation, but continuous wave NIRS users have to rely on the literature values for this parameter.…”
Section: Methodological Considerations and Limitationsmentioning
confidence: 99%