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A Channel Rejection Method for Attenuating Motion-Related Artifacts in EEG Recordings during Walking

Anderson S. Oliveira, Bryan R. Schlink, W. David Hairston, Peter König, Daniel P. Ferris
2017 Frontiers in Neuroscience  
For independent components, the template correlation rejection removed components presenting the majority of epochs correlated to the template.  ...  After rejecting the identified channels and running independent component analysis on the EEG datasets, the proposed method identified 4.3 ± 1.8 independent components (out of 198 ± 12) with substantive  ...  effectiveness of TCR in identifying independent components carrying motion-related artifacts.  ... 
doi:10.3389/fnins.2017.00225 pmid:28491016 pmcid:PMC5405125 fatcat:dhhrj453lrccfdjp5yea6ijusu

Decomposing Effects of Time on Task Reveals an Anteroposterior Gradient of Perceptual Decision Regions

Bradley R. Buchsbaum, Drew T. Erickson, Andrew S. Kayser, Joy J. Geng
2013 PLoS ONE  
Using this novel approach, we identify two such independent components from BOLD activity related to a random dot motion task: one sensitive to the main effect of stimulus duration, and one to both the  ...  However, differentiating decision-related processes from effects of "time on task" can be difficult.  ...  Performed the experiments: BRB DTE ASK. Analyzed the data: BRB ASK. Contributed reagents/materials/analysis tools: BRB DTE ASK. Wrote the paper: ASK. Edited the manuscript: BRB ASK.  ... 
doi:10.1371/journal.pone.0072074 pmid:23977212 pmcid:PMC3747156 fatcat:6bisegoqxzgrfclweaniikopru

Applying Independent Component Analysis to Clinical fMRI at 7 T

Simon Daniel Robinson, Veronika Schöpf, Pedro Cardoso, Alexander Geissler, Florian Ph. S. Fischmeister, Moritz Wurnig, Siegfried Trattnig, Roland Beisteiner
2013 Frontiers in Human Neuroscience  
Task-related activation components could be automatically identified via these intuitive and effective features.  ...  In this study, the ability of independent component analysis (ICA) to separate activation from these artifacts was assessed in a 7 T study of neurological patients performing chin and hand motor tasks.  ...  This was despite the use of effective head fixation, motion correction, and the inclusion of motion parameters in the analysis model.  ... 
doi:10.3389/fnhum.2013.00496 pmid:24032007 pmcid:PMC3759034 fatcat:6vndqvgbhzhmviajg5swkde4fm

Independent component analysis of functional MRI: what is signal and what is noise?

M McKeown
2003 Current Opinion in Neurobiology  
Unlike methods of analysis of fMRI data that test the time course of each voxel against a hypothesized waveform, data-driven methods, such as independent component analysis and clustering, attempt to find  ...  component analysis ROI regions of interest SPM statistical parametric mapping SVD singular value decomposition TR repetition time Independent component analysis of functional MRI McKeown, Hansen and Sejnowski  ...  grant P20 EB 002013 to L Hansen, and both the Howard Hughes Medical Institute and a National Institutes of Health grant MH61619-03 to T Sejnowski.  ... 
doi:10.1016/j.conb.2003.09.012 pmid:14630228 pmcid:PMC2925426 fatcat:6qpybi2xzvexdi23ghce7tocem

Graphical interface for automated management of motion artifact within fMRI acquisitions: INFOBAR

Manish Anand, Jed A. Diekfuss, Alexis B. Slutsky-Ganesh, Scott Bonnette, Dustin R. Grooms, Gregory D. Myer
2020 SoftwareX  
Independent Component Analysis-based Automatic Removal of Motion Artifacts (ICA-AROMA; Pruim et al., 2015) is a robust approach to remove brain activity related to head motion within functional magnetic  ...  INFOBAR also has additional data processing options and visualization features to support all researchers interested in mitigating head motion artifact in post-processing using ICA-AROMA.  ...  Funding sources The authors would like to acknowledge funding support from the National Institutes of Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases, USA grants U01 AR067997  ... 
doi:10.1016/j.softx.2020.100598 pmid:33447655 pmcid:PMC7806167 fatcat:4vbxputruvbnhkr5lzhhyukrbu

Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data

Hirokazu Tanaka, Takusige Katura, Hiroki Sato
2013 NeuroImage  
Our analysis method is referred to as task-related component analysis (TRCA).  ...  The proposed test of statistical significance is found to be independent of the degree of autocorrelation in data if the task duration is sufficiently longer than the temporal scale of autocorrelation,  ...  Sejnowski for his comments on applications of independent component analysis to neuroimaging data. We acknowledge that Dr.  ... 
doi:10.1016/j.neuroimage.2012.08.044 pmid:22922468 fatcat:a7cw3gbxtbdohf2s36ge5a4jvy

Automatic independent component labeling for artifact removal in fMRI

Jussi Tohka, Karin Foerde, Adam R. Aron, Sabrina M. Tom, Arthur W. Toga, Russell A. Poldrack
2008 NeuroImage  
We trained a supervised classifier to distinguish between independent components relating to a potentially task-related signal and independent components clearly relating to structured noise.  ...  After the components had been classified as either signal or noise, a denoised fMR time-series was reconstructed based only on the independent components classified as potentially task-related.  ...  Acknowledgments This work was supported by the Academy of Finland under the grants 204782, 108517, 104834, and 213462 (Finnish Centre of Excellence program (2006-2011)), the National Science Foundation  ... 
doi:10.1016/j.neuroimage.2007.10.013 pmid:18042495 pmcid:PMC2374836 fatcat:ro3naq4ar5bs7knlz7pdtgwvxa

Methods for cleaning the BOLD fMRI signal

César Caballero-Gaudes, Richard C. Reynolds
2017 NeuroImage  
Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches  ...  component analysis.  ...  in R & D [SEV-2015-490], and the research and writing of the paper were supported by the NIMH and NINDS Intramural Research Programs (ZICMH002888) of the NIH/HHS.  ... 
doi:10.1016/j.neuroimage.2016.12.018 pmid:27956209 pmcid:PMC5466511 fatcat:nexqgsdfmvd73grkzcfnrkhesm

EEG Effects of Motion Sickness Induced in a Dynamic Virtual Reality Environment

Chin-Teng Lin, Shang-Wen Chuang, Yu-Chieh Chen, Li-Wei Ko, Sheng-Fu Liang, Tzyy-Ping Jung
2007 IEEE Engineering in Medicine and Biology Society. Conference Proceedings  
The Motion Sickness Questionnaire (MSQ) was used to assess the sickness level, and the EEG effects on the subjects with high sickness levels were investigated using the independent component analysis (  ...  The fake-epoch extraction was then applied to the nausea-related independent components.  ...  It consists of artifacts removal, independent component analysis, useless component rejection, fake-epoch extraction, and event-related spectral perturbation (ERSP).  ... 
doi:10.1109/iembs.2007.4353178 pmid:18002844 fatcat:2jdlgs3dcvckply5i5jf3h32gm

Classification of temporal ICA components for separating global noise from fMRI data: Reply to power

Matthew F. Glasser, Timothy S. Coalson, Janine D. Bijsterbosch, Samuel J. Harrison, Michael P. Harms, Alan Anticevic, David C. Van Essen, Stephen M. Smith
2019 NeuroImage  
We respond to a critique of our temporal Independent Components Analysis (ICA) method for separating global noise from global signal in fMRI data that focuses on the signal versus noise classification  ...  of several components.  ...  Acknowledgements We thank Power 2019 for his perspective on our recent paper about using temporal Independent Components Analysis (ICA) to separate global, largely respiratory, artifact from fMRI data  ... 
doi:10.1016/j.neuroimage.2019.04.046 pmid:31026516 pmcid:PMC6591096 fatcat:kbh4wywxlbezvlnhqe5wz7yusq

Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing

Michael V. Lombardo, Bonnie Auyeung, Rosemary J. Holt, Jack Waldman, Amber N.V. Ruigrok, Natasha Mooney, Edward T. Bullmore, Simon Baron-Cohen, Prantik Kundu
2016 NeuroImage  
Non-BOLD variability identification and removal is achieved in a biophysical and statistically principled manner by combining multi-echo fMRI acquisition and independent components analysis (ME-ICA).  ...  Here we show how specific removal of non-BOLD artifacts can improve effect size estimation and statistical power in task-fMRI contexts, with particular application to the social-cognitive domain of mentalizing  ...  MVL was supported by the Wellcome Trust  ... 
doi:10.1016/j.neuroimage.2016.07.022 pmid:27417345 pmcid:PMC5102698 fatcat:r6ttgaldxzhs7flhjo7zr2lneu

Spatial and temporal EEG dynamics of motion sickness

Yu-Chieh Chen, Jeng-Ren Duann, Shang-Wen Chuang, Chun-Ling Lin, Li-Wei Ko, Tzyy-Ping Jung, Chin-Teng Lin
2010 NeuroImage  
The acquired EEG signals were parsed by independent component analysis (ICA) into maximally independent processes.  ...  This study investigates motion-sickness-related brain responses using a VR-based driving simulator on a motion platform with six degrees of freedom, which provides both visual and vestibular stimulations  ...  This work was in part supported by the Aiming for the Top University Plan of National Chiao-Tung University, the Ministry of Education, Taiwan, under Contract 98W806, and in part supported by the National  ... 
doi:10.1016/j.neuroimage.2009.10.005 pmid:19833217 fatcat:khtkv7xfdzhdlivan5cxmzgkr4

Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals

Prantik Kundu, Valerie Voon, Priti Balchandani, Michael V. Lombardo, Benedikt A. Poser, Peter A. Bandettini
2017 NeuroImage  
At the same time, the field has also dealt with uncertainty related to many known and unknown effects of artifact in fMRI data.  ...  A focus is on the use of ME-fMRI data to extract and classify components from spatial ICA, called multi-echo ICA (ME-ICA).  ...  Acknowledgements PK and PB are supported by the Icahn School of Medicine Capital Campaign, the Translational and Molecular Imaging Institute, Brain Imaging Center and the Department of Radiology at the  ... 
doi:10.1016/j.neuroimage.2017.03.033 pmid:28363836 fatcat:n6mkrdv535gqfnf6nuu2nfzaxy

ICA-based Denoising Strategies in Breath-Hold Induced Cerebrovascular Reactivity Mapping with Multi Echo BOLD fMRI

Stefano Moia, Maite Termenon, Eneko Uruñuela, Gang Chen, Rachael C. Stickland, Molly G. Bright, César Caballero-Gaudes
2021 NeuroImage  
Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal  ...  including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR.  ...  Acknowledgments The authors would like to thank Vicente Ferrer for collaborating in data acquisition and two anonymous reviewers for helping improving the quality of the paper.  ... 
doi:10.1016/j.neuroimage.2021.117914 pmid:33684602 fatcat:5lo2khetwvddtipzlp7ou5aon4

Mixed Signals: On Separating Brain Signal from Noise

Lucina Q. Uddin
2017 Trends in Cognitive Sciences  
Accurate description of human brain function requires the separation of true neural signal from noise.  ...  Recent work examining spatial and temporal properties of whole-brain fMRI signals demonstrates how artifacts from a variety of sources can persist after rigorous processing, and highlights the lack of  ...  Acknowledgments This work was supported by award R01MH107549 from the National Institute of Mental Health to LQU. Uddin Page 3 Trends Cogn Sci. Author manuscript; available in PMC 2018 July 05.  ... 
doi:10.1016/j.tics.2017.04.002 pmid:28461113 pmcid:PMC6033047 fatcat:j3mooqmgcfe6jficnlotwkmdnq
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