Developers / Citations

Distribution and Installation Questions

Jay Dubb, MGH-Martinos Center for Biomedical Imaging

HOMER2_UI Developers

David Boas, MGH-Martinos Center for Biomedical Imaging
Jay Dubb, MGH-Martinos Center for Biomedical Imaging
Ted Huppert, the University of Pittsburgh (Huppert2009)
Meryem Ayse Yucel, MGH-Martinos Center for Biomedical Imaging



Please contact David Boas or Jay Dubb with any algorithms you wish to include in HOMER2.

Massachusetts General Hospital/Harvard Medical School

Louis Gagnon of MIT and the MGH-Martinos Center for Biomedical Imaging contributed code for using short separation measurements to regress physiological interference while simultaneously estimating stimulus evoked hemodynamic responses (Gagnon2011, Gagnon2012). See hmrDeconvTB_3rd(), hmrDeconvTB_SS3rd(), hmrDeconvTB_SS3rd_Highest().

Rob Cooper while at the Martinos Center contributed to several functions related to correcting motion artifacts. See hmrMotionCorrectSpline().

Katherine Perdue of Dartmouth College contributed code for identifying motion artifacts based on a standard deviation threshold to complement the amplitude threshold. See hmrMotionArtifact().

Qianqian Fang of MGH-Martinos Center for Biomedical Imaging contributed matlab routines from his toolboxes iso2mesh and ezmapper to AtlasViewerGUI. The iso2mesh routines are used to resample dense meshes to sparser ones when converting from freesurfer to viewer file formats. The ezmapper library routines are used to convert digitized points acquired with ezmapper tool to the text format which AtlasViewer can read.

Juliette Selb of the Martinos Center contributed to several functions related to correcting motion artifacts. See hmrMotionCorrectSpline().

Sabrina Brigadoi, while visiting from University of Padova, contributed to functions related to correcting motion artifacts. She adopted the wavelet method described by (Molavi2012) for Homer2. See hmrMotionCorrect_Wavelet(). She also contributed hmrMotionCorrect_Cbsi() as adapted from (Cui2010).

Chris Aasted contributed a tool for AtlasViewerGUI that will plot the variability of probe placement across a group of subjects. Assuming you have a group of subjects for which you digitized the opt ode coordinates, this is useful to ascertain if the placement was uniform across subjects.

Meryem Yucel of the Martinos Center contributed to several functions such as correcting motion artifacts using targeted principle component analysis (Yucel2014), ImageRecon toolbox, HbO concentration overlay, hmrnirssegment, hmrNirsFileDownsample, display MNI projection on cortex etc.

Jichi Medical University

Daisuke Tsuzuki and Ippeita (Pepe) Dan (Functional Brain Science Lab) are helping to incorporate their registration tools into Homer2 (Tsuzuki2012). These tools will be part of the AtlasViewerGUI tool.

University Hospital Zurich

Felix Scholkmann and Martin Wolf of the University Hospital of Zurich contributed a motion correction algorithm using splines, as described in (Scholkmann2010). See hmrMotionCorrectionSpline().


Cui, X., Bray, S. and Reiss, A.L. (2010). “Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics.” Neuroimage 49(4): 3039-3046.

Gagnon, L., Perdue, K., Greve, D.N., Goldenholz, D., Kaskhedikar, G. and Boas, D.A. (2011). “Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling.” Neuroimage 56(3): 1362-1371.

Gagnon, L., Cooper, R.J., Yucel, M.A., Perdue, K.L., Greve, D.N. and Boas, D.A. (2012). “Short separation channel location impacts the performance of short channel regression in NIRS.” Neuroimage 59: 2518–2528.

Huppert, T.J., Diamond, S.G., Franceschini, M.A. and Boas, D.A. (2009). “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain.” Appl Opt 48(10): D280-98.

Molavi, B., & Dumont, G. A. (2012). Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiological measurement, 33(2), 259–270.

Scholkmann, F., Spichtig, S., Muehlemann, T., & Wolf, M. (2010). How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. Physiological measurement, 31(5), 649–662.

Tsuzuki, D., Cai, D.-S., Dan, H., Kyutoku, Y., Fujita, A., Watanabe, E., & Dan, I. (2012). Stable and convenient spatial registration of stand-alone NIRS data through anchor-based probabilistic registration. Neuroscience research, 72(2), 163–171.

Yucel, M. A., Selb, J., Cooper, R. J. & Boas, D. A. (2014). Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy. J. Innov. Opt. Health Sci. 7, 1–8.

Aasted, C. M., Yücel, M. A.,  Cooper, R. J., Petkov, M. P., Boas, D. A., Cooper, R. J., Dubb, J., and Tsuzuki, D., (2015). Anatomical guidance for functional near-infrared spectroscopy : AtlasViewer tutorial. Neurophoton., 2(2), 020801.