Identifying appropriate feature to distinguish between resting and active condition from FNIRS

In this paper, a series of numerical analysis is performed on the functional near infrared spectroscopy (FNIRS) data to find out the appropriate feature for investigating the hemodynamic response of human brain. The main objective of this work is to identify the exact feature from FNIR data which can distinguish the resting state and active state of brain. We have analyzed various types of statistical parameters and shown that the effect size is the only differential tools for FNIR analysis to identify the specific channel of activation than the other parameter like average, standard deviation, and total average change in blood volume. Through this research work, two types of effect size from the FNIR data are determined and have shown their characteristics in every detector channel. According to the feature of calculated effect size, it was seen that channel 1 and channel 2 have strong eligibility to differentiate the active and resting condition of brain. Therefore, this study proposes to use channel 1 and channel 2 with a single laser source to design a portable FNIR device to monitor the patient is either in resting or active condition.