Significance: Functional Near Infrared Spectroscopy (fNIRS) is a promising tool for neurofeedback (NFB) or brain computer interfaces (BCIs). However, fNIRS signals are typically highly contaminated by systemic activity (SA) artifacts and, if not properly corrected, NFB or BCIs run the risk of being based on noise instead of brain activity. This risk can likely be reduced by correcting for SA, in particular when short distance channels (SDCs) are available. Literature comparing correction methods with and without SDCs is still sparse, specifically comparisons considering single trials are lacking.
Aim: This study aimed at comparing the performance of SA correction methods with and without SDCs.
Approach: Semi-simulated and real motor task data of healthy elderly individuals were used. Correction methods without SDCs included a simple and a more advanced spatial filter. Correction methods with SDCs included a regression approach considering only the closest SDC and two GLM-based methods, one including all eight SDCs and one using only two a priori selected SDCs as regressors. All methods were compared to data uncorrected for SA and correction performance was assessed with quality measures quantifying signal improvement and spatial specificity at single trial level.
Results: All correction methods were found to improve signal quality and to enhance spatial specificity as compared to the uncorrected data. Methods with SDCs usually outperformed methods without SDCs. Correction methods without SDCs tended to overcorrect the data. However, the exact pattern of results and the degree of differences observable between correction methods varied between semi-simulated and real data, and also between quality measures.
Conclusions: Overall, results confirmed that both Δ[HbO] and Δ[HbR] are affected by SA and that correction methods with SDCs outperform methods without SDCs. Nonetheless, improvements in signal quality can also be achieved without SDCs and should therefore be given priority over not correcting for SA.