Selective Detrending using Baseline Drift Detection Index for Task-dependant fNIRS Signal

Abstract

A functional near-infrared spectroscopy (fNIRS) can be employed to investigate brain activation by measuring the absorption of near-infrared light through the intact skull. The general linear model (GLM) as a standard model for fMRI analysis has been applied to functional near-infrared spectroscopic (fNIRS) imaging analysis as well. The GLM has drawback of failure in fNIRS signals, when they have drift globally. Wavelet based detrending technique is very popular to correct the baseline drift (BD) in fNIRS. However, this method globally distorted the total multi-channel signals even if just one channel’s signal was locally drifted. This paper suggests the selective detrending method using BD detection index to indicate BD as an objective index. The experiments show the performance of the proposed method as graphic results and objective evaluation index with current detrending algorithms.