Most approaches involve using either the waveforms or the temporal frequency characteristic of the global components. The waveform of the global components can be measured with laser-Doppler flowmetry,13 continuous blood pressure,5 or the fNIRS signal from skin using a pair of optodes away.13,14 A general linear regression algorithm has been used to remove these global components from fNIRS data,5,15 and conventional signal processing methods, such as independent component analysis14 and temporal filtering methods,10 have also been applied. These methods assume that the waveform of the task-related neuronal signal is not correlated with the waveform of the global components. However, in some cases, the waveform of the global component is found to be highly correlated with task-related fNIRS signals.16–18 For example, blood pressure and blood flow may show a waveform similar to the expected cortical BOLD signal responding to a task with a block design.5,13 To overcome the challenge that the global component can be highly correlated with the task-induced BOLD signal, an alternative method using the spatial distribution for the removal of global component3,11 has been proposed. Since the only common components between resting state data and the experimental data are the global components, such as respiratory and the blood pressure variation, which have been shown to be highly correlated with the first and second principal components (PCs) of the resting state signal, in theory, we can obtained the spatial pattern of the first and second PCs from the resting state data and remove them from the experimental dataset using a linear regression method.3,11 The mathematical concept of this idea is elegant. However, the global components are relatively weak during the resting state. Therefore, to obtain high-quality global components for subsequent data analysis, prolonged resting state data may be needed. In addition, more than three PCs are often needed to fully describe global components. However, according to the authors, removing more PCs may cause reduction of neural signals related to the tasks.