000172344 001__ 172344 000172344 005__ 20220930130035.0 000172344 0247_ $$2doi$$a10.1371/journal.pone.0112147 000172344 0247_ $$2Handle$$a2128/8083 000172344 0247_ $$2WOS$$aWOS:000344816700034 000172344 0247_ $$2altmetric$$aaltmetric:16200446 000172344 0247_ $$2pmid$$apmid:25383625 000172344 037__ $$aFZJ-2014-05824 000172344 082__ $$a500 000172344 1001_ $$0P:(DE-Juel1)159555$$aMaggioni, Eleonora$$b0$$eCorresponding Author 000172344 245__ $$aRemoval of Pulse Artefact from EEG Data Recorded in MR Environment at 3T. Setting of ICA Parameters for Marking Artefactual Components: Application to Resting-State Data 000172344 260__ $$aLawrence, Kan.$$bPLoS$$c2014 000172344 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s172344 000172344 3367_ $$2DataCite$$aOutput Types/Journal article 000172344 3367_ $$00$$2EndNote$$aJournal Article 000172344 3367_ $$2BibTeX$$aARTICLE 000172344 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000172344 3367_ $$2DRIVER$$aarticle 000172344 520__ $$aSimultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow for a non-invasive investigation of cerebral functions with high temporal and spatial resolution. The main challenge of such integration is the removal of the pulse artefact (PA) that affects EEG signals recorded in the magnetic resonance (MR) scanner. Often applied techniques for this purpose are Optimal Basis Set (OBS) and Independent Component Analysis (ICA). The combination of OBS and ICA is increasingly used, since it can potentially improve the correction performed by each technique separately. The present study is focused on the OBS-ICA combination and is aimed at providing the optimal ICA parameters for PA correction in resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked potential. A comparison between two intervals for ICA calculation and four methods for marking artefactual components was performed. The performance of the methods was discussed in terms of their capability to 1) remove the artefact and 2) preserve the information of interest. The analysis included 12 subjects and two resting-state datasets for each of them. The results showed that none of the signal lengths for the ICA calculation was highly preferable to the other. Among the methods for the identification of PA-related components, the one based on the wavelets transform of each component emerged as the best compromise between the effectiveness in removing PA and the conservation of the physiological neuronal content. 000172344 536__ $$0G:(DE-HGF)POF2-332$$a332 - Imaging the Living Brain (POF2-332)$$cPOF2-332$$fPOF II$$x0 000172344 536__ $$0G:(DE-HGF)POF2-89573$$a89573 - Neuroimaging (POF2-89573)$$cPOF2-89573$$fPOF II T$$x1 000172344 588__ $$aDataset connected to CrossRef, juser.fz-juelich.de 000172344 7001_ $$0P:(DE-Juel1)145041$$aArrubla, Jorge$$b1 000172344 7001_ $$0P:(DE-Juel1)131804$$aWarbrick, Tracy$$b2 000172344 7001_ $$0P:(DE-Juel1)131757$$aDammers, Jürgen$$b3 000172344 7001_ $$0P:(DE-HGF)0$$aBianchi, Anna M.$$b4 000172344 7001_ $$0P:(DE-HGF)0$$aReni, Gianluigi$$b5 000172344 7001_ $$0P:(DE-HGF)0$$aTosetti, Michela$$b6 000172344 7001_ $$0P:(DE-Juel1)131781$$aNeuner, Irene$$b7 000172344 7001_ $$0P:(DE-Juel1)131794$$aShah, N. 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