Preprocessing of fNIRS Data
We used both HbOThe processing was as follows:
Identification and exclusion of bad channels: channels were considered as bad and excluded from the analysis if the coefficient of variation ([standard deviation/mean]*100) of the raw data was > 33%. The function hmrPruneChannels was used (SNRthresh = 3). The exclusion was done subject by subject. For each one, the number of excluded channels was between 0 and 3 out of 18.Optical density conversion: raw data were converted into optical density with the hmrIntensity2OD function.Identification of motion artifacts: time sections were considered as containing motion artifacts if the signal for any given active channel changed by more than 50 times the standard deviation or by more than 5 standard deviations during a 0.5 s period. The hmrMotionArtifactByChannel function was used (tMotion = 0.5, tMask = 1, STDEVthresh = 50, AMPthresh = 5).Motion artifact correction: sections marked as motion artifacts were corrected with principal component analysis, as movement is the principal source of variance. We used the hmrMotionCorrectPCA function (nSv = 0.8).Physiological artifacts were removed using Principal Component with the enPCAfilter_nSV function.Filtering periodic noise: respiration, cardiac activity and high frequency noise were attenuated with hmrBandpassFilt (hpf = 0, lpf = 0.1).Concentration conversion: corrected optical density data were converted into relative concentration changes with the modified Beer-Lambert law (Kocsis et al. Hemodynamic response function (HRF) was estimated by solving a general linear deconvolutionmodel (GLM) using the hmrDeconvTB_SS3rd function (t range = [-10, 20], gstd = 1, gms = 1, rhoSD_ssThresh = 1).