A Finite-Difference Solution for the EEG Forward Problem in Inhomogeneous Anisotropic Media View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Ernesto Cuartas Morales, Carlos D. Acosta-Medina, German Castellanos-Dominguez, Dante Mantini

ABSTRACT

Accurate source localization of electroencephalographic (EEG) signals requires detailed information about the geometry and physical properties of head tissues. Indeed, these strongly influence the propagation of neural activity from the brain to the sensors. Finite difference methods (FDMs) are head modelling approaches relying on volumetric data information, which can be directly obtained using magnetic resonance (MR) imaging. The specific goal of this study is to develop a computationally efficient FDM solution that can flexibly integrate voxel-wise conductivity and anisotropy information. Given the high computational complexity of FDMs, we pay particular attention to attain a very low numerical error, as evaluated using exact analytical solutions for spherical volume conductor models. We then demonstrate the computational efficiency of our FDM numerical solver, by comparing it with alternative solutions. Finally, we apply the developed head modelling tool to high-resolution MR images from a real experimental subject, to demonstrate the potential added value of incorporating detailed voxel-wise conductivity and anisotropy information. Our results clearly show that the developed FDM can contribute to a more precise head modelling, and therefore to a more reliable use of EEG as a brain imaging tool. More... »

PAGES

229-239

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10548-018-0683-2

DOI

http://dx.doi.org/10.1007/s10548-018-0683-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107725194

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30341590


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0299", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Other Physical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/02", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "National University of Colombia", 
          "id": "https://www.grid.ac/institutes/grid.10689.36", 
          "name": [
            "Signal Processing and Recognition Group, Faculty of Engineering, Universidad Nacional de Colombia, Km 9 V\u00eda al Aeropuerto la Nubia, 170001, Manizales, Colombia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cuartas Morales", 
        "givenName": "Ernesto", 
        "id": "sg:person.014367174467.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014367174467.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National University of Colombia", 
          "id": "https://www.grid.ac/institutes/grid.10689.36", 
          "name": [
            "Signal Processing and Recognition Group, Faculty of Engineering, Universidad Nacional de Colombia, Km 9 V\u00eda al Aeropuerto la Nubia, 170001, Manizales, Colombia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Acosta-Medina", 
        "givenName": "Carlos D.", 
        "id": "sg:person.012122321067.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012122321067.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National University of Colombia", 
          "id": "https://www.grid.ac/institutes/grid.10689.36", 
          "name": [
            "Signal Processing and Recognition Group, Faculty of Engineering, Universidad Nacional de Colombia, Km 9 V\u00eda al Aeropuerto la Nubia, 170001, Manizales, Colombia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Castellanos-Dominguez", 
        "givenName": "German", 
        "id": "sg:person.01127171270.96", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01127171270.96"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ospedale San Camillo", 
          "id": "https://www.grid.ac/institutes/grid.416308.8", 
          "name": [
            "Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium", 
            "Functional Neuroimaging Laboratory, IRCCS San Camillo Hospital Foundation, 30126, Venice, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mantini", 
        "givenName": "Dante", 
        "id": "sg:person.0737570314.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0737570314.15"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1155/2011/879716", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000399570"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2010.02.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001069057"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0006-3495(68)86524-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001502524"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2011/156869", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002751766"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2014.08.056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004689870"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2005.10.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005526009"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2011.11.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007761013"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2010/972060", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012963922"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1743-0003-5-25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017641053", 
          "https://doi.org/10.1186/1743-0003-5-25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2015.08.032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023029638"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2014.06.040", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026942020"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clineuro.2013.08.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031780022"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1743-0003-4-46", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036736865", 
          "https://doi.org/10.1186/1743-0003-4-46"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2011.12.039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037642379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinph.2013.04.336", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038650063"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2015.05.052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038717098"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2014/426902", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040807617"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1528-1157.2000.tb01508.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041108417"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1528-1157.2000.tb01508.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041108417"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1012909511833", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043075927", 
          "https://doi.org/10.1023/a:1012909511833"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03178586", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044671892", 
          "https://doi.org/10.1007/bf03178586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03178586", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044671892", 
          "https://doi.org/10.1007/bf03178586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0093154", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046565359"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-18914-7_43", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047913993", 
          "https://doi.org/10.1007/978-3-319-18914-7_43"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.26193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051000187"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/hbm.20795", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052296310"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/hbm.20795", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052296310"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.323779", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057922700"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.341983", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057949167"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/32/1/004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059021497"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/50/16/009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059025687"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/50/18/012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059025731"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/50/18/012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059025731"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/54/20/004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059027856"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/54/20/004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059027856"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/57/11/3517", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059029225"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/10.40805", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061084652"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/10.605429", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061085075"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/10.623049", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061085093"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/10.650347", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061085159"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/10.995679", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061086084"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2016.2624634", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061696839"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/hbm.23688", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086095211"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/bmei.2008.358", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093686778"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iembs.2006.260314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1096109476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iembs.2006.260314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1096109476"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-03", 
    "datePublishedReg": "2019-03-01", 
    "description": "Accurate source localization of electroencephalographic (EEG) signals requires detailed information about the geometry and physical properties of head tissues. Indeed, these strongly influence the propagation of neural activity from the brain to the sensors. Finite difference methods (FDMs) are head modelling approaches relying on volumetric data information, which can be directly obtained using magnetic resonance (MR) imaging. The specific goal of this study is to develop a computationally efficient FDM solution that can flexibly integrate voxel-wise conductivity and anisotropy information. Given the high computational complexity of FDMs, we pay particular attention to attain a very low numerical error, as evaluated using exact analytical solutions for spherical volume conductor models. We then demonstrate the computational efficiency of our FDM numerical solver, by comparing it with alternative solutions. Finally, we apply the developed head modelling tool to high-resolution MR images from a real experimental subject, to demonstrate the potential added value of incorporating detailed voxel-wise conductivity and anisotropy information. Our results clearly show that the developed FDM can contribute to a more precise head modelling, and therefore to a more reliable use of EEG as a brain imaging tool.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10548-018-0683-2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1098975", 
        "issn": [
          "0896-0267", 
          "1573-6792"
        ], 
        "name": "Brain Topography", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "32"
      }
    ], 
    "name": "A Finite-Difference Solution for the EEG Forward Problem in Inhomogeneous Anisotropic Media", 
    "pagination": "229-239", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "52ee6ce42d14a5e850e472c24e3fe19dfa827dfe2c51266e8a8b81a0e9f033c2"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30341590"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "8903034"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10548-018-0683-2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1107725194"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10548-018-0683-2", 
      "https://app.dimensions.ai/details/publication/pub.1107725194"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:13", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000353_0000000353/records_45366_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10548-018-0683-2"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10548-018-0683-2'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10548-018-0683-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10548-018-0683-2'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10548-018-0683-2'


 

This table displays all metadata directly associated to this object as RDF triples.

219 TRIPLES      21 PREDICATES      69 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10548-018-0683-2 schema:about anzsrc-for:02
2 anzsrc-for:0299
3 schema:author Nbb6de567727b4bd6a809dfed6de398e0
4 schema:citation sg:pub.10.1007/978-3-319-18914-7_43
5 sg:pub.10.1007/bf03178586
6 sg:pub.10.1023/a:1012909511833
7 sg:pub.10.1186/1743-0003-4-46
8 sg:pub.10.1186/1743-0003-5-25
9 https://doi.org/10.1002/hbm.20795
10 https://doi.org/10.1002/hbm.23688
11 https://doi.org/10.1002/mrm.26193
12 https://doi.org/10.1016/j.clineuro.2013.08.003
13 https://doi.org/10.1016/j.clinph.2013.04.336
14 https://doi.org/10.1016/j.neuroimage.2005.10.014
15 https://doi.org/10.1016/j.neuroimage.2010.02.014
16 https://doi.org/10.1016/j.neuroimage.2011.11.006
17 https://doi.org/10.1016/j.neuroimage.2011.12.039
18 https://doi.org/10.1016/j.neuroimage.2014.06.040
19 https://doi.org/10.1016/j.neuroimage.2014.08.056
20 https://doi.org/10.1016/j.neuroimage.2015.05.052
21 https://doi.org/10.1016/j.neuroimage.2015.08.032
22 https://doi.org/10.1016/s0006-3495(68)86524-5
23 https://doi.org/10.1063/1.323779
24 https://doi.org/10.1063/1.341983
25 https://doi.org/10.1088/0031-9155/32/1/004
26 https://doi.org/10.1088/0031-9155/50/16/009
27 https://doi.org/10.1088/0031-9155/50/18/012
28 https://doi.org/10.1088/0031-9155/54/20/004
29 https://doi.org/10.1088/0031-9155/57/11/3517
30 https://doi.org/10.1109/10.40805
31 https://doi.org/10.1109/10.605429
32 https://doi.org/10.1109/10.623049
33 https://doi.org/10.1109/10.650347
34 https://doi.org/10.1109/10.995679
35 https://doi.org/10.1109/bmei.2008.358
36 https://doi.org/10.1109/iembs.2006.260314
37 https://doi.org/10.1109/tmi.2016.2624634
38 https://doi.org/10.1111/j.1528-1157.2000.tb01508.x
39 https://doi.org/10.1155/2010/972060
40 https://doi.org/10.1155/2011/156869
41 https://doi.org/10.1155/2011/879716
42 https://doi.org/10.1155/2014/426902
43 https://doi.org/10.1371/journal.pone.0093154
44 schema:datePublished 2019-03
45 schema:datePublishedReg 2019-03-01
46 schema:description Accurate source localization of electroencephalographic (EEG) signals requires detailed information about the geometry and physical properties of head tissues. Indeed, these strongly influence the propagation of neural activity from the brain to the sensors. Finite difference methods (FDMs) are head modelling approaches relying on volumetric data information, which can be directly obtained using magnetic resonance (MR) imaging. The specific goal of this study is to develop a computationally efficient FDM solution that can flexibly integrate voxel-wise conductivity and anisotropy information. Given the high computational complexity of FDMs, we pay particular attention to attain a very low numerical error, as evaluated using exact analytical solutions for spherical volume conductor models. We then demonstrate the computational efficiency of our FDM numerical solver, by comparing it with alternative solutions. Finally, we apply the developed head modelling tool to high-resolution MR images from a real experimental subject, to demonstrate the potential added value of incorporating detailed voxel-wise conductivity and anisotropy information. Our results clearly show that the developed FDM can contribute to a more precise head modelling, and therefore to a more reliable use of EEG as a brain imaging tool.
47 schema:genre research_article
48 schema:inLanguage en
49 schema:isAccessibleForFree false
50 schema:isPartOf N6abacddef4784e668c4ec3b1e376a708
51 Nc3b67e6fc4c84510bada8109ed64f44a
52 sg:journal.1098975
53 schema:name A Finite-Difference Solution for the EEG Forward Problem in Inhomogeneous Anisotropic Media
54 schema:pagination 229-239
55 schema:productId N06965ccd0de942c58c2377d129a26d65
56 N454f7782c7cc4f60b39ab01e118bd198
57 N4880d93f80f34b37b42eb22d890a1e4b
58 N5c91fa9c219f4807a4ac7186a52ee061
59 N842e758662704a968f1eafecff2fe58e
60 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107725194
61 https://doi.org/10.1007/s10548-018-0683-2
62 schema:sdDatePublished 2019-04-11T11:13
63 schema:sdLicense https://scigraph.springernature.com/explorer/license/
64 schema:sdPublisher Nf9c65fee0e404b39912d4c6c5708581b
65 schema:url https://link.springer.com/10.1007%2Fs10548-018-0683-2
66 sgo:license sg:explorer/license/
67 sgo:sdDataset articles
68 rdf:type schema:ScholarlyArticle
69 N06965ccd0de942c58c2377d129a26d65 schema:name nlm_unique_id
70 schema:value 8903034
71 rdf:type schema:PropertyValue
72 N454f7782c7cc4f60b39ab01e118bd198 schema:name doi
73 schema:value 10.1007/s10548-018-0683-2
74 rdf:type schema:PropertyValue
75 N4621d9be6dd74a0f8210993ae49afc90 rdf:first sg:person.012122321067.01
76 rdf:rest Nea14a21fc308404b8974f318200ca92e
77 N4880d93f80f34b37b42eb22d890a1e4b schema:name dimensions_id
78 schema:value pub.1107725194
79 rdf:type schema:PropertyValue
80 N5c91fa9c219f4807a4ac7186a52ee061 schema:name readcube_id
81 schema:value 52ee6ce42d14a5e850e472c24e3fe19dfa827dfe2c51266e8a8b81a0e9f033c2
82 rdf:type schema:PropertyValue
83 N6abacddef4784e668c4ec3b1e376a708 schema:issueNumber 2
84 rdf:type schema:PublicationIssue
85 N842e758662704a968f1eafecff2fe58e schema:name pubmed_id
86 schema:value 30341590
87 rdf:type schema:PropertyValue
88 Nb20f03080aaa47b5a33e2b6e8ff48664 rdf:first sg:person.0737570314.15
89 rdf:rest rdf:nil
90 Nbb6de567727b4bd6a809dfed6de398e0 rdf:first sg:person.014367174467.18
91 rdf:rest N4621d9be6dd74a0f8210993ae49afc90
92 Nc3b67e6fc4c84510bada8109ed64f44a schema:volumeNumber 32
93 rdf:type schema:PublicationVolume
94 Nea14a21fc308404b8974f318200ca92e rdf:first sg:person.01127171270.96
95 rdf:rest Nb20f03080aaa47b5a33e2b6e8ff48664
96 Nf9c65fee0e404b39912d4c6c5708581b schema:name Springer Nature - SN SciGraph project
97 rdf:type schema:Organization
98 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
99 schema:name Physical Sciences
100 rdf:type schema:DefinedTerm
101 anzsrc-for:0299 schema:inDefinedTermSet anzsrc-for:
102 schema:name Other Physical Sciences
103 rdf:type schema:DefinedTerm
104 sg:journal.1098975 schema:issn 0896-0267
105 1573-6792
106 schema:name Brain Topography
107 rdf:type schema:Periodical
108 sg:person.01127171270.96 schema:affiliation https://www.grid.ac/institutes/grid.10689.36
109 schema:familyName Castellanos-Dominguez
110 schema:givenName German
111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01127171270.96
112 rdf:type schema:Person
113 sg:person.012122321067.01 schema:affiliation https://www.grid.ac/institutes/grid.10689.36
114 schema:familyName Acosta-Medina
115 schema:givenName Carlos D.
116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012122321067.01
117 rdf:type schema:Person
118 sg:person.014367174467.18 schema:affiliation https://www.grid.ac/institutes/grid.10689.36
119 schema:familyName Cuartas Morales
120 schema:givenName Ernesto
121 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014367174467.18
122 rdf:type schema:Person
123 sg:person.0737570314.15 schema:affiliation https://www.grid.ac/institutes/grid.416308.8
124 schema:familyName Mantini
125 schema:givenName Dante
126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0737570314.15
127 rdf:type schema:Person
128 sg:pub.10.1007/978-3-319-18914-7_43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047913993
129 https://doi.org/10.1007/978-3-319-18914-7_43
130 rdf:type schema:CreativeWork
131 sg:pub.10.1007/bf03178586 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044671892
132 https://doi.org/10.1007/bf03178586
133 rdf:type schema:CreativeWork
134 sg:pub.10.1023/a:1012909511833 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043075927
135 https://doi.org/10.1023/a:1012909511833
136 rdf:type schema:CreativeWork
137 sg:pub.10.1186/1743-0003-4-46 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036736865
138 https://doi.org/10.1186/1743-0003-4-46
139 rdf:type schema:CreativeWork
140 sg:pub.10.1186/1743-0003-5-25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017641053
141 https://doi.org/10.1186/1743-0003-5-25
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1002/hbm.20795 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052296310
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1002/hbm.23688 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086095211
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1002/mrm.26193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051000187
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1016/j.clineuro.2013.08.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031780022
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1016/j.clinph.2013.04.336 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038650063
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1016/j.neuroimage.2005.10.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005526009
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1016/j.neuroimage.2010.02.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001069057
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1016/j.neuroimage.2011.11.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007761013
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1016/j.neuroimage.2011.12.039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037642379
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1016/j.neuroimage.2014.06.040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026942020
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.neuroimage.2014.08.056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004689870
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1016/j.neuroimage.2015.05.052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038717098
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/j.neuroimage.2015.08.032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023029638
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1016/s0006-3495(68)86524-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001502524
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1063/1.323779 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057922700
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1063/1.341983 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057949167
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1088/0031-9155/32/1/004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059021497
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1088/0031-9155/50/16/009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059025687
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1088/0031-9155/50/18/012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059025731
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1088/0031-9155/54/20/004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059027856
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1088/0031-9155/57/11/3517 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059029225
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1109/10.40805 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061084652
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1109/10.605429 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061085075
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1109/10.623049 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061085093
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1109/10.650347 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061085159
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1109/10.995679 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061086084
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1109/bmei.2008.358 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093686778
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1109/iembs.2006.260314 schema:sameAs https://app.dimensions.ai/details/publication/pub.1096109476
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1109/tmi.2016.2624634 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061696839
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1111/j.1528-1157.2000.tb01508.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1041108417
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1155/2010/972060 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012963922
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1155/2011/156869 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002751766
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1155/2011/879716 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000399570
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1155/2014/426902 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040807617
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1371/journal.pone.0093154 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046565359
212 rdf:type schema:CreativeWork
213 https://www.grid.ac/institutes/grid.10689.36 schema:alternateName National University of Colombia
214 schema:name Signal Processing and Recognition Group, Faculty of Engineering, Universidad Nacional de Colombia, Km 9 Vía al Aeropuerto la Nubia, 170001, Manizales, Colombia
215 rdf:type schema:Organization
216 https://www.grid.ac/institutes/grid.416308.8 schema:alternateName Ospedale San Camillo
217 schema:name Functional Neuroimaging Laboratory, IRCCS San Camillo Hospital Foundation, 30126, Venice, Italy
218 Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium
219 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...