Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI
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منابع مشابه
Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI
Combination of electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) plays a potential role in neuroimaging due to its high spatial and temporal resolution. However, EEG is easily influenced by ballistocardiogram (BCG) artifacts and may cause false identification of the related EEG features, such as epileptic spikes. There are many related methods to remove them,...
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The simultaneous acquisition of electroencephalograpy (EEG) and functional magnetic resonance imaging (fMRI) data is very promising for the study of cognitive processes and disorders but causes severe artifacts in the EEG. In this study the aim is to remove the ballistocardiogram artifact, caused by cardiac pulse-related movements of the electrodes in the magnetic field. For this purpose a meth...
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Electroencephalogram (EEG) data acquired in the MRI scanner contains significant artifacts, one of the most prominent of which is ballistocardiogram (BCG) artifact. BCG artifacts are generated by movement of EEG electrodes inside the magnetic field due to pulsatile changes in blood flow tied to the cardiac cycle. Independent Component Analysis (ICA) is a statistical algorithm that is useful for...
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One of the standard applications of Independent Component Analysis (ICA) to EEG is removal of artifacts due to movements of the eye bulbs. Short blinks as well as slower saccadic movements are removed by subtracting respective independent components (ICs). EEG recorded from blind subjects poses special problems, since it shows a higher quantity of eye movements, which are also more irregular an...
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ژورنال
عنوان ژورنال: Frontiers in Neuroscience
سال: 2018
ISSN: 1662-453X
DOI: 10.3389/fnins.2018.00059