A straight forward signal processing scheme to improve effect size of fNIR signals

摘要

Functional near-infrared spectroscopy (fNIRS) plays an imperative role for studying hemodynamic measurement of brain. Event related task measurement mostly depends on the effect size (ES) of fNIRS data. The noisy fNIR signal is an obstacle to estimate the precise ES of such measurement. Though Savitzky-Golay and Moving Average filters are often used for de-noising the fNIR signal, they have some limitations in measuring ES. In this paper, we have proposed a simple signal processing scheme which contributes to remove noise and evaluates not only proper ES but also overcome the drawback of Savitzky-Golay and Moving Average filter. By this scheme, the filtered signal becomes lower standard deviated than the raw fNIR signal. Else, the scheme maintains the mean of original and filtered signal unchanged. Since, the scheme reduces the standard deviation of the signal notably remaining the mean value unchanged; the ES of interest is improved eloquently. The numerical results and corresponding contrast to noise ratio (CNR) pattern prove the usefulness of the proposed scheme. The numerical results and corresponding contrast to noise ratio (CNR) pattern prove the effectiveness of the proposed scheme.