# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## 4 Discussion
### LF De-Noising Methods  
The ability to isolate brain activity from low-frequency physiological \"noise\" has proven to be complicated. In contrast to high-frequency physiological signals, such as respiration (\(\sim\)0.3 Hz) and cardiac pulsations (1 Hz), which can be excluded by bandpass filtering the data (Figures 3 and 7), LF systemic fluctuations cannot be discarded in that way [24; 48; 51]. LF systemic fluctuations are in the same frequency band as the neuronal signal and travel through the entire body [25]. In addition, the cardiac pulse travels with a very different speed through the brain (closer to a pulse wave) than LF oscillations, stressing the importance of separating removal strategies for these systemic oscillations [24]. Importantly, fNIRS although very similar to fMRI analysis in various domains, cannot necessarily be treated the same as fMRI. Specifically, fNIRS is more prone to systemic fluctuations than MRI is [5; 18], due to the path of the photons through all layers of the head and scalp, which ensures substantial contamination of signals with non-neuronal fluctuations. Conversely, removing higher frequency physiological signals such as cardiac and respiratory fluctuations, which is difficult in fMRI, can be achieved through simple spectral filtering in fNIRS. We tested three common methods that can be used for regression of LF systemic noise, including suboptimal (20 mm) short source-detector (SSD*) recordings, principal component analysis (PCA), and global average (GloAvg) (Example time course, Figure 8). The first two, SSD* and PCA are seldom criticized, yet there is still often the argument against global averaging to produce spurious negative correlations [33]. An advanced de-noising method, which accounts for the time delay between channels, as reported by [PERSON] 2016, has shown to remove systemic global variance without inducing spurious correlations in the data, but has not been implemented for fNIRS data. We filtered the LF noise regressors directly from the data using GLM (PCA) and adaptive filtering (SSD and GloAvg), and evaluated the resulting time courses. We found significant differences in the degree that the LF de-noising methods changed the time course (Figure 9). Specifically, oxyhemoglobin SSD* and GloAvg measures differed greatly from no de-noising, with a correlation of \(r=0.4\). The results of PCA were highly variable (mean correlation of \(r=0.7\)). Furthermore, the SSD method was the only method, which greatly affected deoxyhemoglobin measures (correlation with no de-noising of \(r=0.6\) in comparison to \(r=0.9\) for PCA and GloAvg). This is because SSD measures are separately filtered for each wavelength (after dc conversion). We also found significant higher activation across participants in the ROI for PCA and GloAvg in comparison to no LF de-noising in the combined hemoglobin condition (Figure 11b). However, we did not find many differences in how relevant measures, such as Cohen's d, CNR (Figure 10) or \(t\)-stats (Figure 11) were affected in the ROI. In addition, which channel was the most activated one across participants varied between LF de-noising methods and hemoglobin species (Table 1). Furthermore, changes in the pre-processing step influenced which method looked more promising as well as which channel was most activated (not shown here). From the analysis, we can conclude that the PCA and GloAvg de-noising methods, when adjusted for individual delays, perform similarly in fNIRS data (Figure 11). This is in accordance with multiple MRI studies showing that the first PCA component is most similar to the global average [33] and that when the global average is adjusted for delays it performs identically or can outperform PCA [34]. A disadvantage of PCA and GloAvg is that both are influenced by the channels included and the number of channels available. In large datasets these methods work well, but in small datasets the SSD method is highly advantageous. SSD performed similarly or below PCA and GloAvg.