
@article{biesmann_temporal_2010,
	title = {Temporal kernel {CCA} and its application in multimodal neuronal data analysis},
	volume = {79},
	copyright = {http://www.springer.com/tdm},
	issn = {0885-6125, 1573-0565},
	url = {http://link.springer.com/10.1007/s10994-009-5153-3},
	doi = {10.1007/s10994-009-5153-3},
	language = {en},
	number = {1-2},
	urldate = {2025-03-05},
	journal = {Machine Learning},
	author = {Bießmann, Felix and Meinecke, Frank C. and Gretton, Arthur and Rauch, Alexander and Rainer, Gregor and Logothetis, Nikos K. and Müller, Klaus-Robert},
	month = may,
	year = {2010},
	pages = {5--27},
	file = {PDF:/Users/guybrush/Zotero/storage/ZEAQATYX/Bießmann et al. - 2010 - Temporal kernel CCA and its application in multimodal neuronal data analysis.pdf:application/pdf},
}



@article{witten_penalized_2009,
	title = {A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis},
	volume = {10},
	issn = {1465-4644, 1468-4357},
	url = {https://academic.oup.com/biostatistics/article-lookup/doi/10.1093/biostatistics/kxp008},
	doi = {10.1093/biostatistics/kxp008},
	language = {en},
	number = {3},
	urldate = {2024-07-12},
	journal = {Biostatistics},
	author = {Witten, D. M. and Tibshirani, R. and Hastie, T.},
	month = jul,
	year = {2009},
	pages = {515--534},
	file = {Witten et al. - 2009 - A penalized matrix decomposition, with application.pdf:/Users/guybrush/Zotero/storage/8U7XU9W9/kxp008.pdf:application/pdf},
}


@article{chen_structure-constrained_2013,
	title = {Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis},
	volume = {14},
	issn = {1465-4644, 1468-4357},
	url = {https://academic.oup.com/biostatistics/article-lookup/doi/10.1093/biostatistics/kxs038},
	doi = {10.1093/biostatistics/kxs038},
	abstract = {Motivated by studying the association between nutrient intake and human gut microbiome composition, we developed a method for structure-constrained sparse canonical correlation analysis (ssCCA) in a high-dimensional setting. ssCCA takes into account the phylogenetic relationships among bacteria, which provides important prior knowledge on evolutionary relationships among bacterial taxa. Our ssCCA formulation utilizes a phylogenetic structure-constrained penalty function to impose certain smoothness on the linear coefficients according to the phylogenetic relationships among the taxa. An efficient coordinate descent algorithm is developed for optimization. A human gut microbiome data set is used to illustrate this method. Both simulations and real data applications show that ssCCA performs better than the standard sparse CCA in identifying meaningful variables when there are structures in the data.},
	language = {en},
	number = {2},
	urldate = {2025-01-20},
	journal = {Biostatistics},
	author = {Chen, J. and Bushman, F. D. and Lewis, J. D. and Wu, G. D. and Li, H.},
	month = apr,
	year = {2013},
	pages = {244--258},
	file = {PDF:/Users/guybrush/Zotero/storage/MUIFA2NE/Chen et al. - 2013 - Structure-constrained sparse canonical correlation analysis with an application to microbiome data a.pdf:application/pdf},
}


@article{parkhomenko_sparse_2009,
	title = {Sparse {Canonical} {Correlation} {Analysis} with {Application} to {Genomic} {Data} {Integration}},
	volume = {8},
	url = {https://doi.org/10.2202/1544-6115.1406},
	doi = {doi:10.2202/1544-6115.1406},
	number = {1},
	urldate = {2025-03-21},
	journal = {Statistical Applications in Genetics and Molecular Biology},
	author = {Parkhomenko, Elena and Tritchler, David and Beyene, Joseph},
	year = {2009},
}

@article{Dahne2014,
	title = {{SPoC}: {A} novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters},
	volume = {86},
	issn = {1053-8119},
	url = {https://www.sciencedirect.com/science/article/pii/S1053811913008483},
	doi = {https://doi.org/10.1016/j.neuroimage.2013.07.079},
	abstract = {Previously, modulations in power of neuronal oscillations have been functionally linked to sensory, motor and cognitive operations. Such links are commonly established by relating the power modulations to specific target variables such as reaction times or task ratings. Consequently, the resulting spatio-spectral representation is subjected to neurophysiological interpretation. As an alternative, independent component analysis (ICA) or alternative decomposition methods can be applied and the power of the components may be related to the target variable. In this paper we show that these standard approaches are suboptimal as the first does not take into account the superposition of many sources due to volume conduction, while the second is unable to exploit available information about the target variable. To improve upon these approaches we introduce a novel (supervised) source separation framework called Source Power Comodulation (SPoC). SPoC makes use of the target variable in the decomposition process in order to give preference to components whose power comodulates with the target variable. We present two algorithms that implement the SPoC approach. Using simulations with a realistic head model, we show that the SPoC algorithms are able extract neuronal components exhibiting high correlation of power with the target variable. In this task, the SPoC algorithms outperform other commonly used techniques that are based on the sensor data or ICA approaches. Furthermore, using real electroencephalography (EEG) recordings during an auditory steady state paradigm, we demonstrate the utility of the SPoC algorithms by extracting neuronal components exhibiting high correlation of power with the intensity of the auditory input. Taking into account the results of the simulations and real EEG recordings, we conclude that SPoC represents an adequate approach for the optimal extraction of neuronal components showing coupling of power with continuously changing behaviorally relevant parameters.},
	journal = {NeuroImage},
	author = {Dähne, Sven and Meinecke, Frank C. and Haufe, Stefan and Höhne, Johannes and Tangermann, Michael and Müller, Klaus-Robert and Nikulin, Vadim V.},
	year = {2014},
	keywords = {ASSEP, EEG, MEG, Oscillations, Source power comodulation, SPoC},
	pages = {111--122},
}


@ARTICLE{Dahne2013,
  author={Dähne, Sven and Bießmann, Felix and Meinecke, Frank C. and Mehnert, Jan and Fazli, Siamac and Müller, Klaus-Robert},
  journal={IEEE Transactions on Multimedia}, 
  title={Integration of Multivariate Data Streams With Bandpower Signals}, 
  year={2013},
  volume={15},
  number={5},
  pages={1001-1013},
  keywords={Brain modeling;Electroencephalography;Hemodynamics;Biological system modeling;Neuroimaging;Electrodes;Convolution;EEG;EEG-NIRS;multimodal;neuroimaging;NIRS},
  doi={10.1109/TMM.2013.2250267}}


@article{Li2010A,
	title = {Independent {Component} {Analysis} by {Entropy} {Bound} {Minimization}},
	volume = {58},
	issn = {1053-587X, 1941-0476},
	url = {http://ieeexplore.ieee.org/document/5499122/},
	doi = {10.1109/TSP.2010.2055859},
	abstract = {A novel (differential) entropy estimator is introduced where the maximum entropy bound is used to approximate the entropy given the observations, and is computed using a numerical procedure thus resulting in accurate estimates for the entropy. We show that such an estimator exists for a wide class of measuring functions, and provide a number of design examples to demonstrate its ﬂexible nature. We then derive a novel independent component analysis (ICA) algorithm that uses the entropy estimate thus obtained, ICA by entropy bound minimization (ICA-EBM). The algorithm adopts a line search procedure, and initially uses updates that constrain the demixing matrix to be orthogonal for robust performance. We demonstrate the superior performance of ICA-EBM and its ability to match sources that come from a wide range of distributions using simulated and real-world data.},
	number = {10},
	urldate = {2024-01-09},
	journal = {IEEE Transactions on Signal Processing},
	author = {Li, Xi-Lin and Adali, Tülay},
	month = {Oct},
	year = {2010},
	pages = {5151--5164}
}

@article{Scholkmann2012,
  author = {Scholkmann, Felix and Boss, Jens and Wolf, Martin},
  title = {An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals},
  journal = {Algorithms},
  volume = {5},
  year = {2012},
  number = {4},
  pages = {588--603},
  url = {https://www.mdpi.com/1999-4893/5/4/588},
  issn = {1999-4893},
  doi = {10.3390/a5040588}
}

@article{Fu2014,
	title = {Blind {Source} {Separation} by {Entropy} {Rate} {Minimization}},
	volume = {62},
	issn = {1053-587X, 1941-0476},
	url = {http://ieeexplore.ieee.org/document/6845364/},
	doi = {10.1109/TSP.2014.2333563},
	abstract = {By assuming latent sources are statistically independent, independent component analysis separates underlying sources from a given linear mixture. Since in many applications, latent sources are both non-Gaussian and have sample dependence, it is desirable to exploit both properties jointly. In this paper, we use mutual information rate to construct a general framework for analysis and derivation of algorithms that take both properties into account. We discuss two types of source models for entropy rate estimation—a Markovian and an invertible ﬁlter model—and give the general independent component analysis cost function, update rule, and performance analysis based on these. We also introduce four algorithms based on these two models, and show that their performance can approach the Cramér-Rao lower bound. In addition, we demonstrate that the algorithms with ﬂexible models exhibit very desirable performance for “natural” data.},
	language = {en},
	number = {16},
	urldate = {2022-11-21},
	journal = {IEEE Transactions on Signal Processing},
	author = {Fu, Geng-Shen and Phlypo, Ronald and Anderson, Matthew and Li, Xi-Lin and Adali, Tulay},
	month = {Aug},
	year = {2014},
	pages = {4245--4255}
}

@inproceedings{Li2010B,
	address = {Dallas, TX},
	title = {Blind spatiotemporal separation of second and/or higher-order correlated sources by entropy rate minimization},
	isbn = {978-1-4244-4295-9},
	url = {http://ieeexplore.ieee.org/document/5495311/},
	doi = {10.1109/ICASSP.2010.5495311},
	urldate = {2024-04-17},
	booktitle = {2010 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing}},
	publisher = {IEEE},
	author = {Li, Xi-Lin and Adali, Tulay},
	month = mar,
	year = {2010},
	pages = {1934--1937}
}


@article{Pollonini2014,
  title = {Auditory cortex activation to natural speech and simulated cochlear implant speech measured with functional near-infrared spectroscopy},
  journal = {Hearing Research},
  volume = {309},
  pages = {84-93},
  year = {2014},
  issn = {0378-5955},
  doi = {https://doi.org/10.1016/j.heares.2013.11.007},
  author = {Luca Pollonini and Cristen Olds and Homer Abaya and Heather Bortfeld and Michael S. Beauchamp and John S. Oghalai},
  abstract = {The primary goal of most cochlear implant procedures is to improve a patient's ability to discriminate speech. To accomplish this, cochlear implants are programmed so as to maximize speech understanding. However, programming a cochlear implant can be an iterative, labor-intensive process that takes place over months. In this study, we sought to determine whether functional near-infrared spectroscopy (fNIRS), a non-invasive neuroimaging method which is safe to use repeatedly and for extended periods of time, can provide an objective measure of whether a subject is hearing normal speech or distorted speech. We used a 140 channel fNIRS system to measure activation within the auditory cortex in 19 normal hearing subjects while they listed to speech with different levels of intelligibility. Custom software was developed to analyze the data and compute topographic maps from the measured changes in oxyhemoglobin and deoxyhemoglobin concentration. Normal speech reliably evoked the strongest responses within the auditory cortex. Distorted speech produced less region-specific cortical activation. Environmental sounds were used as a control, and they produced the least cortical activation. These data collected using fNIRS are consistent with the fMRI literature and thus demonstrate the feasibility of using this technique to objectively detect differences in cortical responses to speech of different intelligibility.}
}

@article{Pollonini2016,
	title = {{PHOEBE}: a method for real time mapping of optodes-scalp coupling in functional near-infrared spectroscopy},
	volume = {7},
	issn = {2156-7085, 2156-7085},
	shorttitle = {{PHOEBE}},
	url = {https://opg.optica.org/abstract.cfm?URI=boe-7-12-5104},
	doi = {10.1364/BOE.7.005104},
	abstract = {Recent functional near-infrared spectroscopy (fNIRS) instrumentation encompasses several dozen of optodes to enable reconstructing a hemodynamic image of the entire cerebral cortex. Despite its potential clinical applicability, widespread use of fNIRS with human subjects is currently limited by unresolved issues, namely the collection from the entirety of optical channels of signals with a signal-to-noise ratio (SNR) sufficient to carry out a reliable estimation of cortical hemodynamics, and the considerable amount of time that placing numerous optodes take with individuals for whom achieving good optical coupling to the scalp is difficult due to thick or dark hair. To address these issues, we developed a numerical method that: 1) at the channel level, computes an objective measure of the signalto-noise ratio (SNR) related to its optical coupling to the scalp, akin to electrode conductivity used in electroencephalography (EEG), and 2) at the optode level, determines and displays the coupling status of all individual optodes in real time on a model of a human head. This approach aims to shorten the pre-acquisition preparation time by visually displaying which optodes require further adjustment for optimum scalp coupling, and to maximize the signalto-noise ratio (SNR) of all optical channels contributing to the functional hemodynamic mapping. The methodology described in this paper has been implemented in a software tool named PHOEBE (placing headgear optodes efficiently before experimentation) that is freely available for use by the fNIRS community.},
	number = {12},
	urldate = {2022-11-21},
	journal = {Biomedical Optics Express},
	author = {Pollonini, Luca and Bortfeld, Heather and Oghalai, John S.},
	month = {Dec},
	year = {2016},
	pages = {5104}
}

@article{Huppert2009,
  author = {Theodore J. Huppert and Solomon G. Diamond and Maria A. Franceschini and David A. Boas},
  journal = {Appl. Opt.},
  keywords = {Fourier optics and signal processing ; Medical and biological imaging; Spectroscopy; Functional monitoring and imaging ; Absorption coefficient; Brain imaging; Laser sources; Optical absorption; Optical imaging; Tissue optics},
  number = {10},
  pages = {D280--D298},
  publisher = {Optica Publishing Group},
  title = {HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain},
  volume = {48},
  month = {Apr},
  year = {2009},
  doi = {https://doi.org/10.1364/AO.48.00D280},
  abstract = {Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data.},
  url = "https://github.com/BUNPC/Homer3"
}

@article{Oostenveld2001,
  title = {The five percent electrode system for high-resolution EEG and ERP measurements},
  journal = {Clinical Neurophysiology},
  volume = {112},
  number = {4},
  pages = {713-719},
  year = {2001},
  issn = {1388-2457},
  doi = {https://doi.org/10.1016/S1388-2457(00)00527-7},
  author = {Robert Oostenveld and Peter Praamstra},
  keywords = {Electrode placement, High resolution EEG, High resolution ERP, Nomenclature},
  abstract = {Objective: A system for electrode placement is described. It is designed for studies on topography and source analysis of spontaneous and evoked EEG activity. Method: The proposed system is based on the extended International 10–20 system which contains 74 electrodes, and extends this system up to 345 electrode locations. Results: The positioning and nomenclature of the electrode system is described, and a subset of locations is proposed as especially useful for modern EEG/ERP systems, often having 128 channels available. Conclusion: Similar to the extension of the 10–20 system to the 10–10 system (‘10% system’), proposed in 1985, the goal of this new extension to a 10–5 system is to further promote standardization in high-resolution EEG studies.}
}

@misc{Luke_fNIRS_Finger_Tapping_2021,
  author = {Luke, Robert and McAlpine, David},
  doi = {10.5281/zenodo.5529797},
  month = sep,
  title = {{fNIRS Finger Tapping Data in BIDS Format}},
  version = {v0.0.1},
  year = {2021},
  url = {https://github.com/rob-luke/BIDS-NIRS-Tapping}
}

@article{Huang2013,
  author = {Huang, Yu and Dmochowski, Jacek and Su, Yuzhuo and Datta, Abhishek and Rorden, Chris and Parra, Lucas},
  year = {2013},
  month = {10},
  pages = {066004},
  title = {Automated MRI Segmentation for Individualized Modeling of Current Flow in the Human Head},
  volume = {10},
  journal = {Journal of neural engineering},
  doi = {10.1088/1741-2560/10/6/066004}
}

@misc{Harmening2022,
  author = {Harmening, Nils and Miklody, Daniel}, 
  doi = {10.5281/zenodo.7357674},   
  month = {11},
  title = {{MRIsegmentation}},      
  url = {https://github.com/harmening/MRIsegmentation},
  version = {1.0}, 
  year = {2022}
}   

@article{Gao2015,
  author={Gao, James S.  and Huth, Alexander G.  and Lescroart, Mark D.  and Gallant, Jack L. },
  title={Pycortex: an interactive surface visualizer for fMRI},
  journal={Frontiers in Neuroinformatics},
  volume={9},
  year={2015},
  url={https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2015.00023},
  doi={10.3389/fninf.2015.00023},
  issn={1662-5196},
  abstract={<p>Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical and functional information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software.</p>}
}

@article{Fang2009,
  title={Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units},
  author={Fang, Qianqian and Boas, David A},
  journal={Optics express},
  volume={17},
  number={22},
  pages={20178--20190},
  year={2009},
  publisher={Optica Publishing Group}
}

@article{Yu2018,
  title={Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms},
  author={Yu, Leiming and Nina-Paravecino, Fanny and Kaeli, David and Fang, Qianqian},
  journal={Journal of biomedical optics},
  volume={23},
  number={1},
  pages={010504--010504},
  year={2018},
  publisher={Society of Photo-Optical Instrumentation Engineers}
}

@article{Yan2020,
  title={Hybrid mesh and voxel based Monte Carlo algorithm for accurate and efficient photon transport modeling in complex bio-tissues},
  author={Yan, Shijie and Fang, Qianqian},
  journal={Biomedical Optics Express},
  volume={11},
  number={11},
  pages={6262--6270},
  year={2020},
  publisher={Optica Publishing Group}
}

@article{Dehghani2009,
  title={Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction},
  author={Dehghani, Hamid and Eames, Matthew E and Yalavarthy, Phaneendra K and Davis, Scott C and Srinivasan, Subhadra and Carpenter, Colin M and Pogue, Brian W and Paulsen, Keith D},
  journal={Communications in numerical methods in engineering},
  volume={25},
  number={6},
  pages={711--732},
  year={2009},
  publisher={Wiley Online Library}
}

@article{Tucker2022,
  author = {Stephen Tucker and Jay Dubb and Sreekanth Kura and Alexander von L{\"u}hmann and Robert Franke and J{\"o}rn M. Horschig and Samuel Powell and Robert Oostenveld and Michael L{\"u}hrs and {\'E}douard Delaire and Zahra M. Aghajan and Hanseok Yun and Meryem A. Y{\"u}cel and Qianqian Fang and Theodore J. Huppert and Blaise deB. Frederick and Luca Pollonini and David A. Boas and Robert Luke},
  title = {{Introduction to the shared near infrared spectroscopy format}},
  volume = {10},
  journal = {Neurophotonics},
  number = {1},
  publisher = {SPIE},
  pages = {013507},
  keywords = {functional near-infrared spectroscopy, shared near-infrared spectroscopy format, standardization, data format, data sharing, software, Near infrared spectroscopy, Data acquisition, Neuroimaging, Software development, Standards development, Neurophotonics, Design and modelling, Compliance, Data storage, MATLAB},
  year = {2022},
  doi = {10.1117/1.NPh.10.1.013507},
  URL = {https://doi.org/10.1117/1.NPh.10.1.013507}
}

@misc{Luke2021,
  author = {Luke, Robert and McAlpine, David},
  doi = {10.5281/zenodo.5529797},
  month = sep,
  title = {{fNIRS Finger Tapping Data in BIDS Format}},
  version = {v0.0.1},
  year = {2021},
  url = {https://github.com/rob-luke/BIDS-NIRS-Tapping}
}

@article{Holmes1998,
  title={Enhancement of MR Images Using Registration for Signal Averaging},
  author={Holmes, Colin J. and Hoge, Rick and Collins, Louis and Woods, Roger and Toga, Arthur W. and Evans, Alan C.},
  journal={Journal of Computer Assisted Tomography},
  volume={22},
  number={2},
  pages={324--333},
  year={1998},
  month={March},
  publisher={Lippincott Williams \& Wilkins},
  url={https://journals.lww.com/jcat/abstract/1998/03000/enhancement_of_mr_images_using_registration_for.32.aspx},
  doi={10.1097/00004728-199803000-00032}
}

@article{Sherafati2020,
author = {Sherafati, Arefeh and Snyder, Abraham Z. and Eggebrecht, Adam T. and Bergonzi, Karla M. and Burns-Yocum, Tracy M. and Lugar, Heather M. and Ferradal, Silvina L. and Robichaux-Viehoever, Amy and Smyser, Christopher D. and Palanca, Ben J. and Hershey, Tamara and Culver, Joseph P.},
title = {Global motion detection and censoring in high-density diffuse optical tomography},
journal = {Human Brain Mapping},
volume = {41},
number = {14},
pages = {4093-4112},
keywords = {functional near-infrared spectroscopy, high-density diffuse optical tomography, motion artifact, motion censoring, optical neuroimaging},
doi = {https://doi.org/10.1002/hbm.25111},
year = {2020}
}

@article{Jahani2018,
author = {Sahar Jahani and Seyed K. Setarehdan and David A. Boas and Meryem A. Y{\"u}cel},
title = {{Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky–Golay filtering}},
volume = {5},
journal = {Neurophotonics},
number = {1},
publisher = {SPIE},
pages = {015003},
keywords = {near-infrared spectroscopy, motion artifact, Savitzky–Golay, robust locally weighted regression and smoothing, spline interpolation, Motion detection, Seaborgium, Wavelets, Near infrared spectroscopy, Detection and tracking algorithms, Electronic filtering, Smoothing, Signal detection, Signal to noise ratio, Neurophotonics},
year = {2018},
doi = {10.1117/1.NPh.5.1.015003},
}

@article{Fishburn2019,
title = {Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS},
journal = {NeuroImage},
volume = {184},
pages = {171-179},
year = {2019},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2018.09.025},
url = {https://www.sciencedirect.com/science/article/pii/S1053811918308103},
author = {Frank A. Fishburn and Ruth S. Ludlum and Chandan J. Vaidya and Andrei V. Medvedev},
keywords = {Functional near-infrared spectroscopy, NIRS, Head motion, Artifact, Denoising, Children},
abstract = {Functional near-infrared spectroscopy (fNIRS) is an optical neuroimaging technique of growing interest as a tool for investigation of cortical activity. Due to the on-head placement of optodes, artifacts arising from head motion are relatively less severe than for functional magnetic resonance imaging (fMRI). However, it is still necessary to remove motion artifacts. We present a novel motion correction procedure based on robust regression, which effectively removes baseline shift and spike artifacts without the need for any user-supplied parameters. Our simulations show that this method yields better activation detection performance than 5 other current motion correction methods. In our empirical validation on a working memory task in a sample of children 7–15 years, our method produced stronger and more extensive activation than any of the other methods tested. The new motion correction method enhances the viability of fNIRS as a functional neuroimaging modality for use in populations not amenable to fMRI.}
}

@misc{Fishburn2018,
author = {Fishburn, Frank},
title = {TDDR},
year = {2018},
url = {https://github.com/frankfishburn/TDDR/}
}

@article{Molavi2012,
  title={Wavelet-based motion artifact removal for functional near-infrared spectroscopy},
  author={Molavi, Behnam and Dumont, Guy A},
  journal={Physiological Measurement},
  volume={33},
  number={2},
  pages={259},
  year={2012},
  publisher={Institute of Physics and Engineering in Medicine},
  doi={10.1088/0967-3334/33/2/259}
}

@article{Barker2013,
  author = {Jeffrey W. Barker and Ardalan Aarabi and Theodore J. Huppert},
  journal = {Biomed. Opt. Express},
  keywords = {Image analysis; Physiology; Spectroscopy; Functional monitoring and imaging ; Angiography; Brain imaging; Imaging techniques; Near infrared spectroscopy; Optical absorption; Wavelet transforms},
  number = {8},
  pages = {1366--1379},
  publisher = {Optica Publishing Group},
  title = {Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS},
  volume = {4},
  month = {Aug},
  year = {2013},
  url = {https://opg.optica.org/boe/abstract.cfm?URI=boe-4-8-1366},
  doi = {10.1364/BOE.4.001366},
}