DEKF to Estimate Hemodynamic Response and Path-length in fNIRS Data

Abstract

The removal of the physiological noises and the external noises such as motion artifacts is one of the key issues in functional near-infrared spectroscopy (fNIRS) studies. In this paper, we investigate a dual extended Kalman filter (DEKF) for the data from fNIRS, which consists a state estimator for hemodynamics responses and a parameter estimator for path-lengths of individual channels simultaneously. Eight subjects participated in two experimental sessions of mental arithmetic tasks. The obtained signals are processed by the correlation based signal improvement (CBSI) method and the newly proposed method, respectively. We evaluate accuracy of the hemodynamic response estimation using the correlation between the CBSI estimation and the DEKF estimation while that of the path-lengths using a left-tail $t$ -test on the relative errors between sessions. The noise-resistant performance is validated using the metrics of correlation coefficient analysis and within-subject standard deviation (SD). The results show that the DEKF-converted concentrations strongly correlate with that of CBSI and the estimated path-lengths are statistically identical in both sessions. The DEKF method is as resistant to noise as CBSI.