HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain.

By Theodore J. Huppert, S. G. Diamond, Maria Angela Franceschini, David A. Boas. Applied Optics, 2009

1,687 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2dc8ac23bb1d47bf58e55d8a5ba804ec2b14a8cf

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy (2012, methodology): “The motion artifact identification algorithm hmrMotionArtifact from the HOMER2 NIRS processing package1 (Huppert et al., 2009)”
fNIRS-based brain-computer interfaces: a review (2015, methodology): “Physiological noises include those due to heartbeat (1 ∼ 1.5 Hz), respiration (0.2 ∼ 0.5 Hz), Mayer waves ( ∼ 0.1 Hz), which are related to blood pressure ﬂuctuations (Boas et al., 2004a,b; Zhang et al., 2005; Franceschini et al., 2006; Huppert et al., 2009).”
Artifact Removal in Physiological Signals—Practices and Possibilities (2012, background): “There are three distinct sources of artifacts that impact all physiological recordings; environmental artifacts, experimental errors, and systemic physiological artifacts [32].”
Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy. (2010, methodology): “…freeware packages are available for the analysis of optical signals each with the flexibility to accommodate different system configurations (HomER, Huppert et al., 2009; fOSA, Koh, et al., 2007; NIRS-SPM, Ye et al., 2008), however some unification and standardization of data analysis…”
Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data (2014, methodology): “The data processing was performed using some of the Homer2 NIRS processing package functions (Huppert et al., 2009) based in MATLAB (Mathworks, MA USA).”
A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology (2014, methodology): “• For data evaluation, some manufacturers offer proprietary software, others make their software available and others prefer the use of open-source toolboxes such as HomER2 (Huppert et al., 2009; PMILab, 2012), NIRS-SPM, or NIRFAST (Dehghani et al.”
Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS (2018, positive): “Simulated fNIRS signals were corrected using TDDR and 5 different comparison methods detailed below, as implemented in Homer2 (Huppert et al., 2009).”
Statistical analysis of fNIRS data: A comprehensive review (2014, positive): “Inspired by the success of GLM as well as the standard samplewise statistical testing, Huppert et al. (2009) introduced the first public domain fNIRS analysis package, called HomER.”
Anatomical guidance for functional near-infrared spectroscopy: AtlasViewer tutorial (2015, background): “AtlasViewer is part of the near-infrared spectroscopy software analysis package Homer, 15 which can be downloaded for free at Ref.”
The NIRS Brain AnalyzIR Toolbox (2018, methodology): “The current toolbox interfaces to both the popular HOMER-2 [19] and NIRS-SPM [20] fNIRS analysis packages, which allows a head-to-head comparison of various analysis options.”