{"data":{"id":"10.4231/r7qf8qt4","type":"dois","attributes":{"doi":"10.4231/r7qf8qt4","prefix":"10.4231","suffix":"r7qf8qt4","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Castellanos, Miguel","givenName":"Miguel","familyName":"Castellanos","affiliation":["School of Electrical and Computer Engineering, Purdue University"],"nameIdentifiers":[]},{"name":"Liu, Zhongming","givenName":"Zhongming","familyName":"Liu","affiliation":["Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, Purdue University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8773-4204","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Signal Processing Toolbox for Simultaneously Acquired fMRI and EEG (Version 1)"}],"publisher":"Purdue University Research Repository","container":{},"publicationYear":2015,"subjects":[{"subject":"Bioinformatics"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Singular value decomposition"},{"subject":"Electrical and Computer Engineering"},{"subject":"Signal Processing"},{"subject":"independent component analysis"},{"subject":"fMRI-EEG"},{"subject":"Biomedical Engineering"},{"subject":"FOS: Medical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2015-07-16","dateType":"Available"},{"date":"2015-07-16","dateType":"Submitted"},{"date":"2015-07-27","dateType":"Accepted"},{"date":"2015","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Datasets","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsPreviousVersionOf","relatedIdentifier":"10.4231/r7db7zsc","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["72 files","62 MB"],"formats":["text/plain","application/octet-stream","text/x-c","application/x-dosexec","application/x-sharedlib","application/x-mach-binary","biosig/brainvision"],"version":"1.0","rightsList":[{"rights":"GNU General Public License v3.0 only","rightsUri":"https://www.gnu.org/licenses/gpl-3.0-standalone.html","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"gpl-3.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"\u0026lt;p\u0026gt;EEG signals simultaneously acquired during fMRI are noisy and contain artifacts that primarily arise from MRI gradient switching and cardiac pulsation. This toolbox includes a set of open-source Matlab functions implementing several published algorithms for removing such artifacts from EEG. These functions can be called individually or through a graphic user interface (GUI) compatible with the widely used EEG processing software (EEGLab).\u0026lt;/p\u0026gt;\n\n\u0026lt;p\u0026gt;This work is supported by the National Institute of Mental Health through a grant (R01-MH104402) to Dr. Zhongming Liu.\u0026lt;/p\u0026gt;","descriptionType":"Abstract"},{"description":"This Matlab toolbox includes signal processing functions to remove gradient and pulse artifacts in EEG data recorded simultaneously with fMRI. It is distributed as a GUI plugin for EEGLAB.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"Multimodal Hyperspectral Imaging of Brain Activity and Connectivity","funderName":"National Institute of Mental Health, National Institutes of 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