These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
177 related articles for article (PubMed ID: 20441799)
1. ICA-based muscle artefact correction of EEG data: what is muscle and what is brain? Comment on McMenamin et al. Olbrich S; Jödicke J; Sander C; Himmerich H; Hegerl U Neuroimage; 2011 Jan; 54(1):1-3; discussion 4-9. PubMed ID: 20441799 [TBL] [Abstract][Full Text] [Related]
5. Muscle artifact removal from human sleep EEG by using independent component analysis. Crespo-Garcia M; Atienza M; Cantero JL Ann Biomed Eng; 2008 Mar; 36(3):467-75. PubMed ID: 18228142 [TBL] [Abstract][Full Text] [Related]
6. A practical guide to the selection of independent components of the electroencephalogram for artifact correction. Chaumon M; Bishop DV; Busch NA J Neurosci Methods; 2015 Jul; 250():47-63. PubMed ID: 25791012 [TBL] [Abstract][Full Text] [Related]
7. ICA-based reduction of electromyogenic artifacts in EEG data: comparison with and without EMG data. Gabsteiger F; Leutheuser H; Reis P; Lochmann M; Eskofier BM Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():3861-4. PubMed ID: 25570834 [TBL] [Abstract][Full Text] [Related]
8. Removing artefacts from TMS-EEG recordings using independent component analysis: importance for assessing prefrontal and motor cortex network properties. Rogasch NC; Thomson RH; Farzan F; Fitzgibbon BM; Bailey NW; Hernandez-Pavon JC; Daskalakis ZJ; Fitzgerald PB Neuroimage; 2014 Nov; 101():425-39. PubMed ID: 25067813 [TBL] [Abstract][Full Text] [Related]
9. Removal of pulse artefact from EEG data recorded in MR environment at 3T. Setting of ICA parameters for marking artefactual components: application to resting-state data. Maggioni E; Arrubla J; Warbrick T; Dammers J; Bianchi AM; Reni G; Tosetti M; Neuner I; Shah NJ PLoS One; 2014; 9(11):e112147. PubMed ID: 25383625 [TBL] [Abstract][Full Text] [Related]
10. Muscle artifacts in multichannel EEG: characteristics and reduction. Ma J; Tao P; Bayram S; Svetnik V Clin Neurophysiol; 2012 Aug; 123(8):1676-86. PubMed ID: 22240418 [TBL] [Abstract][Full Text] [Related]
12. Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. De Clercq W; Vergult A; Vanrumste B; Van Paesschen W; Van Huffel S IEEE Trans Biomed Eng; 2006 Dec; 53(12 Pt 1):2583-7. PubMed ID: 17153216 [TBL] [Abstract][Full Text] [Related]
13. A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings. Tamburro G; Fiedler P; Stone D; Haueisen J; Comani S PeerJ; 2018; 6():e4380. PubMed ID: 29492336 [TBL] [Abstract][Full Text] [Related]
17. Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis. Gao J; Zheng C; Wang P Clin EEG Neurosci; 2010 Jan; 41(1):53-9. PubMed ID: 20307017 [TBL] [Abstract][Full Text] [Related]
18. Improved quality of auditory event-related potentials recorded simultaneously with 3-T fMRI: removal of the ballistocardiogram artefact. Debener S; Strobel A; Sorger B; Peters J; Kranczioch C; Engel AK; Goebel R Neuroimage; 2007 Jan; 34(2):587-97. PubMed ID: 17112746 [TBL] [Abstract][Full Text] [Related]
19. Detection of EEG spatial-spectral-temporal signatures of errors: a comparative study of ICA-based and channel-based methods. Shou G; Ding L Brain Topogr; 2015 Jan; 28(1):47-61. PubMed ID: 25228153 [TBL] [Abstract][Full Text] [Related]
20. A fully automatic ocular artifact suppression from EEG data using higher order statistics: improved performance by wavelet analysis. Ghandeharion H; Erfanian A Med Eng Phys; 2010 Sep; 32(7):720-9. PubMed ID: 20466582 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]