# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## Results
### LF De-Noising Methods  
We applied three commonly used low frequency (LF) de-noising methods, namely short distance adaptive filtering (SSD*), principal component analysis (PCA) and delay-corrected global averaging (GloAvg). A visualization of an example time course of subject #1 in channel 12 can be seen in Figure 8.  
Figure 9 shows how similar the time courses of the relevant ROI channels are (here channel 12 for all participants). Against no LF de-noising method, we found low to moderate correlations with the oxyhemoglobin time courses (with SSD* \(r\) = 0.44 \(\pm\) 0.1, with PCA \(r\) = 0.70 \(\pm\) 0.2, with GloAvg \(r\) = 0.41 \(\pm\) 0.1) and moderate to high correlation with the deoxyhemoglobin time courses (with SSD* \(r\) = 0.57 \(\pm\) 0.1, with PCA \(r\) = 0.95 \(\pm\) 0.1, with GloAvg \(r\) = 0.92 \(\pm\) 0.1). Between each other, SSD* time courses were moderately correlated with PCA (\(r\) = 0.56 \(\pm\) 0.1; \(r\) = 0.56 \(\pm\) 0.1 for oxy- and deoxyhemoglobin, respectively) and GloAvg (\(r\) = 0.54 \(\pm\) 0.2; \(r\) = 0.54 \(\pm\) 0.1) time courses. PCA and GloAvg time courses were highly correlated within oxygenated (\(r\) = 0.77 \(\pm\) 0.2) and deoxygenated hemoglobin (\(r\) = 0.94 \(\pm\) 0.1). When comparing similarity of the LF de-noising methods to no de-noising, significant differences with oxygenated hemoglobin were found between the de-noising methods SSD* and PCA (\(p_{\rm c}\) = 0.0005) and GloAvg and PCA (\(p_{\rm c}\) = 0.0001) but not between SSD* and GloAvg (\(p_{\rm c}\) = 0.41). In deoxygenated hemoglobin, differences were found between SSD and PCA (\(p_{\rm c}\) < 0.05), as well as between SSD* and GloAvg (\(p_{\rm c}\) < 0.05), but not between PCA and GloAvg (\(p_{\rm c}\) = 0.17). Figure 7: Filters. (**A**) Comparison between the standard HOMER2 Butterworth filter (BW#1) and a Butterworth filter of the same order designed in MATLAB (BW#2), as well as a zero-delay trapezoidal FFT filter (FFT). (**B**) Visualization of power spectra of the random data, (**C**) filtered with all three filters (BW#1 = blue, BW#2 = red, FFT = yellow). Enlargements of all filters in (**C**) can be seen in (**D**) for BW#1, (**E**) for BW#2 and (**F**) for FFT. Figure 8: Low-frequency (LF) de-noising methods. Visualization of three commonly used LF de-noising methods, namely short distance adaptive filtering (SSD*, 20 mm source-detector distance), principal component analysis (PCA) and delay-corrected global averaging (GloAvg). Example of changes in oxyhemoglobin from subject #1 in channel 12. Shaded sections depict finger tapping. Figure 9: Similarity of LF de-noising methods. (**A**) Correlation between time courses after LF de-noising, with example ROI channel 12. Upper triangle (red frame) depicts mean oxyhemoglobin correlation between the time courses of the different methods and lower triangle (blue frame) shows deoxyhemoglobin. (**B**) Correlation of LF de-noising methods with no de-noising with example ROI channel 12 for oxy- (red) and deoxyhemoglobin (blue). To evaluate if there is a meaningful difference between no de-noising and the LF de-noising methods, we calculated the maximum Cohen's d (Figure 10a) and contrast to noise ratio (CNR) (Figure 10b) in the region of interest (ROI, channels 4, 5, 10 and 12) for each subject. No significant difference was found for [PERSON]'s d. Significant lower CNR values were found with the SSD* method in comparison to the PCA method (\(p_{\text{c}}\) = 0.046) with oxyhemoglobin and with the SSD* method in comparison to GloAvg (\(p_{\text{c}}\) = 0.04) with deoxyhemoglobin. Mean maximum [PERSON]'s d in oxy- and deoxyhemoglobin, respectively was: for no de-noising 0.80 \(\pm\) 0.6 and \(-\)0.78 \(\pm\) 0.7; for SSD* 0.75 \(\pm\) 0.6 and \(-\)0.74 \(\pm\) 0.7; for PCA 0.98 \(\pm\) 0.8 and \(-\)0.85 \(\pm\) 0.7; for AvgGlo 0.84 \(\pm\) 0.7 and \(-\)0.89 \(\pm\) 0.7.