How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation

TLDR

Near‑infrared imaging non‑invasively measures brain hemodynamics but is highly susceptible to movement artifacts, and no general reduction method exists. The study aimed to develop a new method to reduce movement artifacts in NIRI data. The authors devised a semi‑automatic approach using moving‑standard‑deviation detection and spline interpolation, validated on simulated and real NIRI signals. The method substantially reduced artifacts, improved signal quality and evoked‑response detection, and is applicable to other artifact‑containing signals such as ECG and EEG.

Abstract

Near-infrared imaging (NIRI) is a neuroimaging technique which enables us to non-invasively measure hemodynamic changes in the human brain. Since the technique is very sensitive, the movement of a subject can cause movement artifacts (MAs), which affect the signal quality and results to a high degree. No general method is yet available to reduce these MAs effectively. The aim was to develop a new MA reduction method. A method based on moving standard deviation and spline interpolation was developed. It enables the semi-automatic detection and reduction of MAs in the data. It was validated using simulated and real NIRI signals. The results show that a significant reduction of MAs and an increase in signal quality are achieved. The effectiveness and usability of the method is demonstrated by the improved detection of evoked hemodynamic responses. The present method can not only be used in the postprocessing of NIRI signals but also for other kinds of data containing artifacts, for example ECG or EEG signals.