# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## 1 Introduction
### Filtering and De-Noising for Removal of Systemic Physiology  
As discussed by [PERSON] and [PERSON] [4], a major concern when performing functional NIRS activation studies is the possibility of producing \"false positives\" and/or \"false negatives\" by inadvertently measuring physiological components that are non-neuronal in origin, and interpreting the hemodynamic changes as neurovascular coupling [4]. This is particularly troublesome when using \"black box\" software (software that cannot be accessed in instrument developer software or software that is not understood [4]), which does not account for global or systemic physiology, as is common practice for new fNIRS users [3]. fNIRS recordings are contaminated by both systemic physiology and extracerebral tissue (skin, bone, and cerebrospinal fluid) [15; 16; 17; 18]. To avoid producing confounded results, it is helpful to employ measures to reduce the influence of extracerebral tissue and systemic physiology, and increase the weighting of the signal to cerebral tissue. Bandpass filtering is commonly used to remove high-frequency (\(\geq\)0.2 Hz; heartbeat, respiratory rate, instrument noise) and very low (\(<\)0.01 Hz) frequency information from the signal [8]. Several different filters have been implemented in fNIRS studies for noise removal, with the majority using the Butterworth filter. Other filters used include Chebyshev, elliptic, and zero-phase Fast Fourier Transform (FFT) filters. However, no absolute advantage of one over the other has been observed. For a review on this topic, and on advanced filters that take additional physiological measurements into account, see [PERSON] and [PERSON] [19]. For the purposes of this study, we offer only a simple visual comparison of two Butterworth filters, and the zero-phase FFT filter. Even after bandpass filtering, physiological noise with frequencies that overlap neurovascular coupling frequencies remain in the fNIRS time-series data and can significantly alter results. The low-frequency (LF; 0.01-0.15 Hz) component of the signal is affected by changes in blood pressure, vasoreactivity, CO\({}_{2}\) concentration, and the autonomic nervous system (ANS), and therefore global or systemic changes can be inadvertently interpreted as neurovascular coupling [4; 20]. Furthermore, it has been shown that LF NIRS fluctuations recorded in the periphery are highly correlated with LF fNIRS fluctuations recorded from the surface of the head, as well as LF BOLD fluctuations in the brain, suggesting a major component of the LF hemodynamic signal is of non-neuronal origin [21; 22; 23; 24; 25; 26]. A visual example of the different frequency patterns that comprise a typical fNIRS signal is provided in Figure 3, applying a bandpass filter at different frequency bands. A number of approaches have been developed to increase the sensitivity of fNIRS to neurovascular coupling by removing global physiology. Comprehensive reviews of the aforementioned sources of systemic noise, and the methods available to mitigate them, have been summarized by [PERSON] and colleagues [27] and [PERSON] and colleagues [28]. Three of the more common methods used to separate the neurovascular component from the systemic noise include short source-detector (SSD) regression to remove skin and bone blood flow, and removal of global covariance by either principal component analysis (PCA) or global average (GloAvg). The SSD channel is believed to predominantly measure extracerebral hemodynamics that occur in the skin and bone, and therefore removal of this component increases sensitivity to neurovascular coupling [29; 30]. PCA for fNIRS signal processing is data-driven approach that statistically identifies the signal components that explain the greatest amount of spatial covariance (covariance across all spatially distributed channels), and then removes those components from all channels, which also results in a signal that is weighted more towards neurovascular coupling than systemic physiology [31; 32].