Wavelet-based method for removing global physiological noise in functional near-infrared spectroscopy

Abstract

Functional near-infrared spectroscopy (fNIRS) is a fast-developing non-invasive functional brain imaging technology widely used in cognitive neuroscience, clinical research and neural engineering. However, it is a challenge to effectively remove the global physiological noise in the fNIRS signal. The global physiological noise in fNIRS arises from multiple physiological origins in both superficial tissues and the brain. It has complex temporal, spatial and frequency characteristics, casting significant influence on the results. In the present study, we developed a novel wavelet-based method for fNIRS global physiological noise removal. The method is data-driven and does not rely on any additional hardware or subjective noise component selection procedure. It consists of two steps. Firstly, we use wavelet transform coherence to automatically detect the time-frequency points contaminated by the global physiological noise. Secondly, we decompose the fNIRS signal by using the wavelet transform, and then suppress the wavelet energy of the contaminated time-frequency points. Finally, we transform the signal back to a time series. We validated the method by using simulation and real data at both task-and resting-state. The results showed that our method can effectively remove the global physiological noise from the fNIRS signal and improve the spatial specificity of the task activation and the resting-state functional connectivity pattern.

Signal Processing in fNIRS: A Case for the Removal of Systemic Activity for Single Trial Data

“…However, depending on the context of an experiment (e.g., online vs. offline) filter types [e.g., finite impulse response (FIR) vs. infinite filter response (IIR); Pinti et al, 2018] and cutoff frequencies differ substantially (e.g, offline filter with cutoff frequencies of [0.01, 0.09] Hz as in Pinti et al, 2018 vs. online filter with cutoff frequencies of [0.01, 1.5] Hz as in Kober et al, 2014). Since the exact frequency characteristics of the systemic components are unknown and might differ between subjects, band-pass filtering might not eliminate all frequencies related to this physiological contamination (Duan et al, 2018). One possible approach to overcome this limitation might be the application of wavelet filters since in this context it is not relevant to know the exact frequency components (Jang et al, 2009; Duan et al, 2018).…”

Spectral clustering-based resting-state network detection approach for functional near-infrared spectroscopy

“…These global noise components may cause fake high correlations in the RSFC matrix and influence the spectral clustering results. Therefore, we suggest that the data should be preprocessed with global physiological noise removal methods (e.g., [32]). Second, the RSN component determination in our method is essentially based on looking for public connectivity clusters for all the participants.…”

“…The study protocol was approved by the Institutional Review Board at Shenzhen Key Laboratory of Affective and Social Neuroscience, Shenzhen University. The data was from a previously published study for other purpose [32].…”

“…We compared the performance of the two methods in detecting the sensorimotor RSN by using the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) [9]. The sensorimotor template derived from the channel localization was used as the ground truth to draw the ROC curve [32]. Figure 2 illustrated the results of the simulated data containing different RSN number.…”

Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip Task

“…An alternative method would be to employ short-distance channels (<1 cm), which were not available in the commercial optode holder cap used, to remove by regression hemodynamic fluctuations that co-occur in the cortex as well as superficial scalp layers (Gagnon et al, 2012;Tachtsidis and Scholkmann, 2016). Although we used bandpass filters and PCA to remove physiological inferences and global hemodynamic fluctuations, there exists several other different computational methods including (i) independent component analysis, (ii) singular value decomposition (SVD) and Gaussian kernel smoothing, (iii) statistical correction methods, (iv) wavelet-based methods, or (v) a combination of these methods can be used for removal (Huppert et al, 2009;Jang et al, 2009;Tachtsidis and Scholkmann, 2016;Cao et al, 2018b;Duan et al, 2018;Klein and Kranczioch, 2019). Thus, a quantitative comparison using different global component removal methods is warranted in future studies.…”