Data-Driven Calibration for Wearable Brain Functional Imaging Devices

Recent advancements in near-infrared spectroscopy (NIRS) and associated optical techniques have significantly contributed to the development of wearable neuroimaging devices capable of capturing real-time neuronal activity with enhanced spatial and temporal resolution. However, despite these advancements, calibration remains a persistent challenge due to variability in NIRS sensor design, which can significantly degrade data quality. In this paper we introduce a universal data-driven calibration method designed to enhance the precision and reliability of NIRS sensor measurement. The proposed method integrates gradient descent–based optimisation with constraint-guided clustering to iteratively minimise calibration errors under realistic usage conditions. To evaluate its effectiveness, the algorithm was tested using an in-silico phantom constructed in MATLAB/NIRFAST, demonstrating notable improvements in signal clarity and haemoglobin concentration estimation. Additionally, the approach exhibits robustness to motion artefacts, thereby improving measurement fidelity. These contributions advance the reliability and accessibility of wearable brain imaging systems, enabling broader applications in both neuroscience research and clinical diagnostics.