Investigating functional near-infrared spectroscopy signal variability: the role of processing pipelines and task complexity

Functional near-infrared spectroscopy (fNIRS) is a portable, non-invasive brain imaging method with growing applications in neurorehabilitation. However, signal variability, driven in part by differences in data processing pipelines, remains a major barrier to its clinical adoption. This study compares the robustness of two common processing approaches, General Linear Model (GLM) and Block Averaging (BA), in detecting cortical activation across task complexities. Eighteen neurotypical, healthy adults completed a simple hand grasp task and a more complex gross manual dexterity task while fNIRS data were recorded and analyzed using the BA and GLM pipelines. Results revealed significant effects of both pipeline and task complexity on oxygenated and deoxygenated hemoglobin amplitudes. BA produced significantly larger responses than GLM, and complex tasks elicited significantly greater activation than simple tasks. Notably, only the BA-Complex subgroup showed significant differences from all other conditions, suggesting BA more effectively detects task-related hemodynamic changes. These findings emphasize the need for careful analysis pipeline selection to reduce variability and enhance fNIRS reliability in neurorehabilitation research.