Subtle changes in either breathing pattern or cardiac pulse rate alter blood oxygen level dependent functional magnetic resonance imaging signal (BOLD fMRI). This is problematic because such fluctuations could possibly not be related to underlying neuronal activations of interest but instead the source of physiological noise. Several methods have been proposed to eliminate physiological noise in BOLD fMRI data. One such method is to derive a template based on average multi-subject data for respiratory response function (RRF) and cardiac response function (CRF) by simultaneously utilizing an external recording of cardiac and respiratory waveforms with the fMRI. Standard templates can then be used to model, map, and remove respiration and cardiac fluctuations from fMRI data. Utilizing these does not, however, account for intra-subject variations in physiological response. Thus, performing a more individualized approach for single subject physiological noise correction becomes more desirable, especially for clinical purposes. Here we propose a novel approach that employs subject-specific RRF and CRF response functions obtained from the whole brain or brain tissue-specific global signals (GS). Averaging multiple voxels in global signal computation ensures physiological noise dominance over thermal and system noise in even high-spatial-resolution fMRI data, making the GS suitable for deriving robust estimations of both RRF and CRF for individual subjects. Using these individualized response functions instead of standard templates based on multi-subject averages judiciously removes physiological noise from the data, assuming that there is minimal neuronal contribution in the derived individualized filters. Subject-specific physiological response functions obtained from the GS better maps individuals' physiological characteristics.