Multi-Channel-Based Differential Pathlength Factor Estimation for Continuous-Wave fNIRS

Functional near-infrared spectroscopy (fNIRS) in brain imaging needs to be robust subject-wise variability. The use of a fixed differential pathlength factor (DPF) per wavelength for the entire will degrade accuracy hemodynamic responses. Since tissue composition varies within brain, correct DPF values should used various emitter-detector distances and regions. In this article, estimation method combining state-space model modified Beer-Lambert law (mBLL), parameter estimating reduced scattering coefficients, dual square-root cubature Kalman filters (SCKFs) is proposed. To validate proposed method, known light intensities (six channels, two wavelengths) reference DPFs are generated using NIRFAST (a Matlab toolbox) presumed paradigm, properties, Balloon model, finite element head consisting 58,818 mesh elements. Then, estimated Jacobian matrix from mBLL. results show that concentration changes correlate well with data. Also, showed relative errors less than 1.33% maximum 0.75% on average. A one-tailed $t$ -test revealed matched more 99.9% confidence. developed can efficiently access actual even if vary significantly properties not uniform. With models SCKFs, real-time one experiment another has become plausible.