Functional Near-Infrared Spectroscopy (fNIR) is an optical brain monitoring technology that tracks changes in hemodynamic responses within the cortex. fNIR uses specific wavelengths of light, introduced at the scalp, to enable the noninvasive measurement of changes in the relative ratios of deoxygenated hemoglobin (deoxy-Hb) and oxygenated hemoglobin (oxy-Hb) during brain activity. This technology allows the design of portable, safe, affordable, noninvasive, and minimally intrusive monitoring systems that can be used to measure brain activity in natural environments, ambulatory and field conditions. However, for such applications fNIR signals can get prone to noise due to motion of the head. Improving signal quality and reducing noise, can be especially challenging for real time applications. Here, we study motion artifact related noise especially due to poor and changing sensor coupling. We have developed a simple and iterative method that can be used to automate the preprocessing of data to identify segments with such noise for exclusion and this method is also suitable for real time applications.