# Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications
## 3 Results
### Motion Artifact Results  
Limited to Hybrid-MARA and SC-MARA and MC-MARA, the detection of the MA windows is compared in Figure 3. The percentage of the signal labelled MA was similarly low in both Hybrid-MARA (1.10-0.90%, median-IQR), which is a SC-method, and SC-MARA (0.32-0.42%, median-IQR). The two methods, conversely, diverged concerning the average length of labeled windows (1.73-0.57 s for HybridMARA, 3.15-0.36 s for SC-MARA) and the former showed a more focused MA detection, due to the combination of both the local std (equal to MARA) and a gradient criterion.  
With respect to MC-MARA, it is worth recalling that this variant was implemented in this study to test the possible crosstalk of MAs among fNIRS channels. The percentage of the signal labelled MA, after the MC \"OR\" logical operator, was abruptly increased (6.46-5.85%, which was about 4-fold compared to SC), thus showing a large independence among such artifacts in the various channels. The increased length of labelled tracts (3.84-1.79 s) is a trivial consequence of this observation. This result does suggest that SC MA reduction methods are well justified.  
It is also worth noting that boxplots provided in Figure 3 only refer to the identification step of the HybridMARA, MC- and SC-MARA algorithms. The results are identical between the HybridMARA-tPCA SG vs. HybridMARA-splineSG algorithms, as well as the MC/SC-MARA-tPCA vs. MC/SC-MARA-spline versions. Conversely, no boxplots were provided for the Wavelet and TDDR methods since these algorithms do act on the overall trend of the signal instead of separate segments. Therefore, computing the motion artifact length and percentage over processed signals will give results close to 100% and the overall signal length. As a consequence, only three boxplots per motion artifact feature were presented in Figure 3.  
In Figure 4, an example of MA reduction across the eight algorithms is shown comparing the signal before (i.e., blue line) and after MA correction (i.e., red line). For the first six methods, the labelling of MA windows (1 for MA detected, yellow) is also added. This last aspect is not presented for the last two since they analyze the overall features of the signal.  
Largely different behaviors in baseline drift correction are also shown, which evidently emphasizes the need for the subsequent BPF step, with respect to the HPF action. Interestingly, all methods efficiently detected local anomalies, such as the MA spike at about 340 s. However, large differences are shown in the removal action. Namely, all six MAR  
Figure 4: Single-channel graphical representation of the effect of MA reduction over \(\Delta OD\) signals across pipelines. \(\Delta OD\) signals are represented with respect to the unprocessed signal (blue line-input signal) and after MA reduction (red line-output signal). Additionally, signal tracts labelled as MAs are presented as squared waves (yellow line).  
Figure 3: Boxplot comparison of averaged MA percentage (**left**) and temporal length (**right**) across subjects and measurement channels. Cross symbols outside interquartile ranges indicate data outliers.  
variants appeared to exert heavy interpolation, which might eliminate useful signals if occurring at high rates throughout the whole recording. Obviously, this risk might be hugely amplified in MC logic. A more conservative cleaning is conversely shown by the TDDR and the Wavelet methods.