NIRS (or fNIRS; functional-near-infrared-spectroscopy) is a non-invasive neuroimaging method, based on light in the near-infrared spectrum to measure the
hemodynamic response function (HRF) in the cortex and therefore infer cerebral activity. A
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NIRS system consists of source optodes, directing the light into the head, and detector optodes, which are positioned several centimeters apart and measure the intensity of back scattered light. From these changes in signal intensity, HbO and HbR can be calculated (Huppert, Diamond, Franceschini, & Boas, 2009; Scholkmann et al., 2014). The temporal resolution of NIRS is quite good, better than in fMRI, but lower than in EEG. Conversely, the spatial resolution is relatively poor, better than in EEG, but much lower than in fMRI
(Gagnon et al., 2011). As the light reaches maximally 3 centimeters inside the head, NIRS is limited to activity changes in upper cortical layers only (Chen et al., 2020). A huge advantage of NIRS is its portability. Therefore, it can be used in different settings inside and outside of the laboratory; it is cheap in its maintenance, easy to apply and relatively robust against body movements. Thus, NIRS systems are gaining importance especially in the study of motor and MI tasks (Batula et al., 2017) and in the use with challenging populations that might be unsuitable for fMRI measurements (e.g., infants or stroke patients; Di Lorenzo et al., 2019;
Obrig, 2014; Sood, McLaughlin, & Cortez, 2015).
1.3.1 Artifacts
Although NIRS is a relatively robust method considering movements of the body, there are some limitations to the method, resulting in unsystematic variations present in the signal. The light emitted by the source probe travels from the surface of the scalp to the cortical layer of the brain. On its way to the cortex and back, the light passes through the upper layers of the scalp and the skull. As a result, the signal contains not only the hemodynamic response resulting from neuronal activation, but also physiological noise present in the extra cortical layers. The noise arises from the cardiac signal, oscillations in arterial blood pressure (Meyer waves), respiration as well as other physiological changes in the extra cortical layers (Kirilina et al., 2012). Cardiac and respiratory signals can easily be filtered out of the NIRS signal whereas lower frequency changes, such as Meyer waves, are hard to detect and can correlate with the signal (Brigadoi et al., 2014; Gagnon, Yücel, Boas, &
Cooper, 2014; Jahani, Setarehdan, Boas, & Yücel, 2018).
Additionally, the NIRS signal can also contain motion artifacts, appearing when the optodes are losing contact with the skull. To ensure constant contact, the NIRS probes should be mounted on a cap, fitting tightly to the head of subjects or glued directly to the skin
(Gagnon et al., 2014; Yücel, Selb, Boas, Cash, & Cooper, 2014). Despite proper mounting, motion artifacts can still occur due to motions of the scalp resulting from eyebrow or jaw
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movements or movements close to the head. Motion artifacts occur in different shapes, frequencies, content, and different timing. Generally, there are three different types of shapes in the signal: Spikes, baseline shifts and low frequency variations. Further, motion artifacts can occur as isolated events or they can be temporally correlated with the hemodynamic response function, due to task related movements and physiological changes (Jahani et al., 2018). For instance, jaw movements, as carried out during speaking or swallowing, lead to massive temporal muscle activity and can produce task related motion artifacts in speech studies (Schecklmann, Mann, Langguth, & Ehlis, 2017). Hence, motion artifacts can be either identified easily or hard to distinguish from the signal, too. As motion artifacts differ in their appearance, the efficacy of correction techniques varies with the type of motion artifacts and the choice of the best method is data dependent (Brigadoi et al., 2014; Cooper et al., 2012;
Yücel et al., 2021).
1.3.2 Artifact Correction
So, what options are there to gain a clear, artifact free signal? The first possibility is to exclude identified artifacts from the signal. Obviously, this implies a loss of data, which can be especially problematic in single trials, when the artifacts correlate with the stimulus onset or when dealing with data with large numbers of artifacts are present. The second option is the correction of the signal. Roughly, two different types of approaches can be distinguished:
Those relying only on the data commonly collected with NIRS and those which require additional sources (Chen et al., 2020; Nguyen, Yoo, Bhutta, & Hong, 2018; Yücel et al., 2021).
1.3.2.1 Correction Methods
As motion artifacts can significantly reduce the number of useful trials, distort the initial HRF and thereby introduce artificial effects, it is crucial to correct the signal (Selb et al., 2015). Correction methods aim to restore the original hemodynamic response resulting from brain activation for the parts of the signal where motion artifacts have been identified and excluded. There are several approved mathematical approaches, but no established standard procedure, as the best method depends on type and number of motion artifacts (Brigadoi et al., 2014). The following sections presents an overview of commonly used correction techniques.
18 Principal Component Analysis (PCA)
PCA uses an orthogonal transformation to gain uncorrelated components, equal to the number of measurements in the original data set and related to its variance. The components are arranged in descending order regarding the proportion of variance they explain. As motion artifacts often evoke bigger changes in amplitude, it is presumed that the first couple of components represent the artifacts, causing the variance in the signal. Therefore, those components should be removed and consequently ensure correction for the motion artifacts.
(Zhang, 2005). The components to be removed can either be fixed, chosen for each subject separately or by defining a specific percentage of variance as threshold for removal. PCA is quite effective for data, in which motion artifacts are the main source of variance and consist out of high amplitude spikes (e.g. in studies with infants), but in data where the amplitude of motion artifacts is similar to the one of the cortical signal, it tends to remove too much variance of the evoked hemodynamic response (Brigadoi et al., 2014). To handle this
problem, PCA can be applied to segments of data, identified as motion artifacts, only (tPCA;
Yücel, Selb, Cooper, & Boas, 2014).
Spline Interpolation
Spline interpolation (Scholkmann, Spichtig, Muehlemann, & Wolf, 2010) is a channel-by-channel approach, which only operates on identified motion artifacts instead of the entire signal. First, the motion artifacts have to be detected by a reliable technique, before a
polynomial model is fitted into the period of the motion artifact and then subtracted from the original signal. Afterwards, the time series has to be reconstructed, based on the mean of the signal section following the artifact and the mean of the previous section. Spline interpolation is capable to correct for baseline shifts in the signal, however, if the artifacts cannot be
appropriately detected, the technique will not improve the signal quality. (Brigadoi et al., 2014)
Wavelet Filtering
Wavelet filtering constitutes another channel-by-channel approach, based on the assumption, that the components of the signal function follow a normal distribution, whereas outliers will be due to motion artifacts (Molavi & Dumont, 2012). Outliers are defined by the interquartile range, which can be set depending on the amount and type of motion artifacts
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(Brigadoi et al., 2014; Di Lorenzo et al., 2019). The coefficients of the outliers are set to zero and the original signal is recomposed with an inversed discrete wavelet transformation.
Wavelet filtering allows to maintain most of the frequency content and is a suitable method to correct for spikes (Brigadoi et al., 2014; Yücel et al., 2021). Moreover, it has proven efficient in a speech study, where motion artifacts occurred in dependence of the stimuli (Brigadoi et al., 2014). On the other hand, wavelet filtering does not correct for baseline shifts, it takes a lot of time to be computed compared to other techniques, and the parameter determining outliers varies between populations (Brigadoi et al., 2014; Yücel et al., 2021).
Correlation-based Signal Improvement (CBSI)
CBSI is based on the assumptions that HbO and HbR are negatively correlated during functional activation and positively correlated in the presence of motion artifacts (Cui, Bray,
& Reiss, 2010). Further, the true physiological signal and the motion artifact are assumed to be uncorrelated. To correct for motion artifacts, CBSI simply forces a negative correlation between HbO and HbR. This approach is effective especially when motion artifacts occur simultaneously with stimulus onsets and as it is quite simple, it also enables online correction of the signal during measurements. CBSI is limited due to the assumptions it relies on. A negative correlation between HbO and HbR may not always be present during cortical activation and therefore CBSI itself can produce artifacts in the HRF (Brigadoi et al., 2014).
As results from previous NIRS studies indicate that HbO and HbR are not inversely related during active swallowing (see section 1.2.2; Kober & Wood, 2014, 2018), CBSI may not be a useful motion artifact correction method for swallowing.
1.3.2.2 Additional Sources
Additional sources allow to extract the interfering measure from the NIRS signal. This can be any auxiliary external instrument, that measures physiological parameters (Raggam, Bauernfeind, & Wriessnegger, 2020), for instance, a pulse sensor, a respiratory belt or EMG (Chen et al., 2020; Kirilina et al., 2012, 2013). Another source which is recently gaining attention are so called short-separation, or short-distance (SD) channels (Gagnon et al., 2011;
Saager & Berger, 2008). SD-channels result from the observation that the depth of the NIRS signal varies as a function of the distance between source and detector optodes. The further corresponding probes are apart from each other, the deeper the light travels into the brain, along with a simultaneous increase in the number of artifacts (see section 1.3.1; Chen et al.,
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2020; Cooper et al., 2012; Gagnon et al., 2011). Whereas normal NIRS detectors are about 3 cm apart from the corresponding emitter (so-called long-distance channels), SD detectors are placed close to the light source. Thus, the emitted light reaches extracortical layers only (e.g., scalp, skull) before being detected by a SD-detector. As regular long-distance channels contain information from both, the cortex and the extra-cortical, measuring multiple distances at the same time enables to regress the signal from the extra-cortical layers from the cortical activation (Brigadoi & Cooper, 2015; Gagnon et al., 2011).