Near infrared spectroscopy (NIRS), an emerging non-invasive tool for functional neuroimaging, has evolved as a multichannel technique allowing simultaneous measurements through many channels ranging from below ten to above hundred. Simultaneous testing of such a large number of channels escalates the risk of Type I error, therefore multiplicity correction is unavoidable. To date, only a few studies have considered this issue using Bonferroni correction, which is an effective conservative solution, but may be too severe for neuroimaging. Its power varies in inverse proportion of the number of channels, which varies among NIRS studies depending on selected region of interest (ROI), thereby leading to a subjective inference. This problem may be well circumvented by a more contemporary approach, called false discovery rate (FDR) that is widely being adopted in functional neuroimaging. An FDR-based procedure controls the expected proportion of erroneously rejected hypotheses among the rejected hypotheses, which offers a more objective, powerful, and consistent measure of Type I error than Bonferroni correction and maintains a better balance between power and specificity. In this technical note, we examine FDR approach using examples from simulated and real NIRS data. The FDR-based procedure could yield 52% more power than Bonferroni correction in a 172-channel real NIRS study and proved to be more robust against the changing number of channels.