Current Practices in Epilepsy Monitoring; Future Prospects and the ARMOR Challenge View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Andreas A. Ioannides , Vahe Poghosyan , George K. Kostopoulos

ABSTRACT

Methods and hardware for EEG recording are advancing rapidly with novel solutions approaching the performance of EEG recordings with conventional electrodes. The prime mover of the new solutions is the electronic game industry. Although there are some common desirable characteristics needed by EEG measurements in both the game industry and for home monitoring of epilepsy there are differences too, primarily in the demands for high signal quality, and this is where the new solutions are naturally still insufficient for clinical applications. Advances in the signal analysis are more mature with the use of tomographic estimates of activity from MEG but also from EEG data opening new ways for advancing the capability and usefulness of home monitoring for epilepsy management. The combination of the advances in EEG hardware and data analysis together with genetic and anatomical information for individual subjects coupled to powerful data mining techniques for “big data” is likely to revolutionize the monitoring and management of epilepsy. More... »

PAGES

87-109

Book

TITLE

Cyberphysical Systems for Epilepsy and Related Brain Disorders

ISBN

978-3-319-20048-4
978-3-319-20049-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-20049-1_5

DOI

http://dx.doi.org/10.1007/978-3-319-20049-1_5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005916139


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "Laboratory for Human Brain Dynamics, AAI Scientific Cultural Services Ltd."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ioannides", 
        "givenName": "Andreas A.", 
        "id": "sg:person.0600434417.84", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0600434417.84"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Laboratory for Human Brain Dynamics, AAI Scientific Cultural Services Ltd."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Poghosyan", 
        "givenName": "Vahe", 
        "id": "sg:person.01145340017.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145340017.81"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Neurophysiology Unit, Department of Physiology, Medical School, University of Patras"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kostopoulos", 
        "givenName": "George K.", 
        "id": "sg:person.0645757700.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0645757700.02"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.jneumeth.2012.05.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001475794"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1475-925x-9-45", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004223453", 
          "https://doi.org/10.1186/1475-925x-9-45"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2006.11.052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004951963"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2006.11.052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004951963"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2012.07.058", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005196462"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fnins.2011.00053", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009258039"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-02577-8_68", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012487634", 
          "https://doi.org/10.1007/978-3-642-02577-8_68"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1523/jneurosci.1091-05.2005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015109357"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fneur.2012.00114", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015152662"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fnhum.2014.00182", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015323219"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/adhm.201300311", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015659032"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/machines2010087", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028484132"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2012.01.121", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028815754"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1741-2560/8/2/025008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029194097"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fnins.2012.00060", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032048425"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/s140712847", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032678446"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2011/923703", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036020403"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1523/jneurosci.0552-13.2014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037611035"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0266-5611/6/4/005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041998952"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1475-925x-3-25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045262556", 
          "https://doi.org/10.1186/1475-925x-3-25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2008.04.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047377074"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2008.04.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047377074"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinph.2004.10.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048438007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1024/1662-9647/a000014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056331714"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/42.759120", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061170758"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.1969.4502613", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061523547"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2014.2347318", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061529650"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2004.837363", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061694650"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.21014/acta_imeko.v3i3.94", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068830589"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4028/www.scientific.net/ast.96.102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072049487"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iembs.2007.4353650", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077517882"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iembs.2008.4650549", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077840038"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iembs.2011.6090895", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078503035"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/embc.2013.6610665", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078797400"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iscas.2008.4541835", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093847048"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/bsn.2010.52", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094956752"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/bsn.2010.52", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094956752"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015", 
    "datePublishedReg": "2015-01-01", 
    "description": "Methods and hardware for EEG recording are advancing rapidly with novel solutions approaching the performance of EEG recordings with conventional electrodes. The prime mover of the new solutions is the electronic game industry. Although there are some common desirable characteristics needed by EEG measurements in both the game industry and for home monitoring of epilepsy there are differences too, primarily in the demands for high signal quality, and this is where the new solutions are naturally still insufficient for clinical applications. Advances in the signal analysis are more mature with the use of tomographic estimates of activity from MEG but also from EEG data opening new ways for advancing the capability and usefulness of home monitoring for epilepsy management. The combination of the advances in EEG hardware and data analysis together with genetic and anatomical information for individual subjects coupled to powerful data mining techniques for \u201cbig data\u201d is likely to revolutionize the monitoring and management of epilepsy.", 
    "editor": [
      {
        "familyName": "Voros", 
        "givenName": "Nikolaos S.", 
        "type": "Person"
      }, 
      {
        "familyName": "Antonopoulos", 
        "givenName": "Christos P.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-20049-1_5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-20048-4", 
        "978-3-319-20049-1"
      ], 
      "name": "Cyberphysical Systems for Epilepsy and Related Brain Disorders", 
      "type": "Book"
    }, 
    "name": "Current Practices in Epilepsy Monitoring; Future Prospects and the ARMOR Challenge", 
    "pagination": "87-109", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-20049-1_5"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "1c2f59f94260d48ba4441fc6b3ec6e6b0adfac90039162588a58d4ee16efe8a6"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1005916139"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-20049-1_5", 
      "https://app.dimensions.ai/details/publication/pub.1005916139"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T16:14", 
    "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/v90/0000000001_0000000264/records_8675_00000246.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-319-20049-1_5"
  }
]
 

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.10.1007/978-3-319-20049-1_5'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-20049-1_5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-20049-1_5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-20049-1_5'


 

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

192 TRIPLES      23 PREDICATES      61 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-20049-1_5 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N6334a7fbe979471dadadd5ae9402af3f
4 schema:citation sg:pub.10.1007/978-3-642-02577-8_68
5 sg:pub.10.1186/1475-925x-3-25
6 sg:pub.10.1186/1475-925x-9-45
7 https://doi.org/10.1002/adhm.201300311
8 https://doi.org/10.1016/j.clinph.2004.10.001
9 https://doi.org/10.1016/j.jneumeth.2012.05.011
10 https://doi.org/10.1016/j.neuroimage.2006.11.052
11 https://doi.org/10.1016/j.neuroimage.2012.01.121
12 https://doi.org/10.1016/j.neuroimage.2012.07.058
13 https://doi.org/10.1016/j.neuron.2008.04.013
14 https://doi.org/10.1024/1662-9647/a000014
15 https://doi.org/10.1088/0266-5611/6/4/005
16 https://doi.org/10.1088/1741-2560/8/2/025008
17 https://doi.org/10.1109/42.759120
18 https://doi.org/10.1109/bsn.2010.52
19 https://doi.org/10.1109/embc.2013.6610665
20 https://doi.org/10.1109/iembs.2007.4353650
21 https://doi.org/10.1109/iembs.2008.4650549
22 https://doi.org/10.1109/iembs.2011.6090895
23 https://doi.org/10.1109/iscas.2008.4541835
24 https://doi.org/10.1109/tbme.1969.4502613
25 https://doi.org/10.1109/tbme.2014.2347318
26 https://doi.org/10.1109/tmi.2004.837363
27 https://doi.org/10.1155/2011/923703
28 https://doi.org/10.1523/jneurosci.0552-13.2014
29 https://doi.org/10.1523/jneurosci.1091-05.2005
30 https://doi.org/10.21014/acta_imeko.v3i3.94
31 https://doi.org/10.3389/fneur.2012.00114
32 https://doi.org/10.3389/fnhum.2014.00182
33 https://doi.org/10.3389/fnins.2011.00053
34 https://doi.org/10.3389/fnins.2012.00060
35 https://doi.org/10.3390/machines2010087
36 https://doi.org/10.3390/s140712847
37 https://doi.org/10.4028/www.scientific.net/ast.96.102
38 schema:datePublished 2015
39 schema:datePublishedReg 2015-01-01
40 schema:description Methods and hardware for EEG recording are advancing rapidly with novel solutions approaching the performance of EEG recordings with conventional electrodes. The prime mover of the new solutions is the electronic game industry. Although there are some common desirable characteristics needed by EEG measurements in both the game industry and for home monitoring of epilepsy there are differences too, primarily in the demands for high signal quality, and this is where the new solutions are naturally still insufficient for clinical applications. Advances in the signal analysis are more mature with the use of tomographic estimates of activity from MEG but also from EEG data opening new ways for advancing the capability and usefulness of home monitoring for epilepsy management. The combination of the advances in EEG hardware and data analysis together with genetic and anatomical information for individual subjects coupled to powerful data mining techniques for “big data” is likely to revolutionize the monitoring and management of epilepsy.
41 schema:editor N09e1e2dc7eaf4482ad29ac5b2b22408a
42 schema:genre chapter
43 schema:inLanguage en
44 schema:isAccessibleForFree false
45 schema:isPartOf N8edd6720e48e485a831dfeedcfd444a5
46 schema:name Current Practices in Epilepsy Monitoring; Future Prospects and the ARMOR Challenge
47 schema:pagination 87-109
48 schema:productId N9a878de1d9e54203afb92c39544d5be5
49 Nbe460124978b4602a89221b8020a0047
50 Nc40f450889574b3cabab24197d54d5e6
51 schema:publisher Na776d4a49bd042679f5382ae81738149
52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005916139
53 https://doi.org/10.1007/978-3-319-20049-1_5
54 schema:sdDatePublished 2019-04-15T16:14
55 schema:sdLicense https://scigraph.springernature.com/explorer/license/
56 schema:sdPublisher Nabb05c14ae6840e08db7a3bcded06221
57 schema:url http://link.springer.com/10.1007/978-3-319-20049-1_5
58 sgo:license sg:explorer/license/
59 sgo:sdDataset chapters
60 rdf:type schema:Chapter
61 N09e1e2dc7eaf4482ad29ac5b2b22408a rdf:first Nd750e55ac2cd40aba9e58b5fc59731ba
62 rdf:rest N52704e190e894a269591292275b56937
63 N27e4554f57204f159ee78597d215eef4 schema:name Neurophysiology Unit, Department of Physiology, Medical School, University of Patras
64 rdf:type schema:Organization
65 N37db493a6d794454a01a7e39cb566486 schema:name Laboratory for Human Brain Dynamics, AAI Scientific Cultural Services Ltd.
66 rdf:type schema:Organization
67 N50275b3889954a8eae6518a0c24a3a1b schema:name Laboratory for Human Brain Dynamics, AAI Scientific Cultural Services Ltd.
68 rdf:type schema:Organization
69 N52704e190e894a269591292275b56937 rdf:first Nc5a6ab4aae7d4b53adf1a2500408dd85
70 rdf:rest rdf:nil
71 N6334a7fbe979471dadadd5ae9402af3f rdf:first sg:person.0600434417.84
72 rdf:rest Nededcfa96b2948eeac741f9907accdd5
73 N8edd6720e48e485a831dfeedcfd444a5 schema:isbn 978-3-319-20048-4
74 978-3-319-20049-1
75 schema:name Cyberphysical Systems for Epilepsy and Related Brain Disorders
76 rdf:type schema:Book
77 N9a878de1d9e54203afb92c39544d5be5 schema:name doi
78 schema:value 10.1007/978-3-319-20049-1_5
79 rdf:type schema:PropertyValue
80 Na776d4a49bd042679f5382ae81738149 schema:location Cham
81 schema:name Springer International Publishing
82 rdf:type schema:Organisation
83 Nabb05c14ae6840e08db7a3bcded06221 schema:name Springer Nature - SN SciGraph project
84 rdf:type schema:Organization
85 Nbe460124978b4602a89221b8020a0047 schema:name dimensions_id
86 schema:value pub.1005916139
87 rdf:type schema:PropertyValue
88 Nc40f450889574b3cabab24197d54d5e6 schema:name readcube_id
89 schema:value 1c2f59f94260d48ba4441fc6b3ec6e6b0adfac90039162588a58d4ee16efe8a6
90 rdf:type schema:PropertyValue
91 Nc5a6ab4aae7d4b53adf1a2500408dd85 schema:familyName Antonopoulos
92 schema:givenName Christos P.
93 rdf:type schema:Person
94 Nccd40d5248994c328ef0ee6a941fa093 rdf:first sg:person.0645757700.02
95 rdf:rest rdf:nil
96 Nd750e55ac2cd40aba9e58b5fc59731ba schema:familyName Voros
97 schema:givenName Nikolaos S.
98 rdf:type schema:Person
99 Nededcfa96b2948eeac741f9907accdd5 rdf:first sg:person.01145340017.81
100 rdf:rest Nccd40d5248994c328ef0ee6a941fa093
101 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
102 schema:name Information and Computing Sciences
103 rdf:type schema:DefinedTerm
104 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
105 schema:name Artificial Intelligence and Image Processing
106 rdf:type schema:DefinedTerm
107 sg:person.01145340017.81 schema:affiliation N37db493a6d794454a01a7e39cb566486
108 schema:familyName Poghosyan
109 schema:givenName Vahe
110 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145340017.81
111 rdf:type schema:Person
112 sg:person.0600434417.84 schema:affiliation N50275b3889954a8eae6518a0c24a3a1b
113 schema:familyName Ioannides
114 schema:givenName Andreas A.
115 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0600434417.84
116 rdf:type schema:Person
117 sg:person.0645757700.02 schema:affiliation N27e4554f57204f159ee78597d215eef4
118 schema:familyName Kostopoulos
119 schema:givenName George K.
120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0645757700.02
121 rdf:type schema:Person
122 sg:pub.10.1007/978-3-642-02577-8_68 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012487634
123 https://doi.org/10.1007/978-3-642-02577-8_68
124 rdf:type schema:CreativeWork
125 sg:pub.10.1186/1475-925x-3-25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045262556
126 https://doi.org/10.1186/1475-925x-3-25
127 rdf:type schema:CreativeWork
128 sg:pub.10.1186/1475-925x-9-45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004223453
129 https://doi.org/10.1186/1475-925x-9-45
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1002/adhm.201300311 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015659032
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/j.clinph.2004.10.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048438007
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/j.jneumeth.2012.05.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001475794
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/j.neuroimage.2006.11.052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004951963
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1016/j.neuroimage.2012.01.121 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028815754
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1016/j.neuroimage.2012.07.058 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005196462
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1016/j.neuron.2008.04.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047377074
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1024/1662-9647/a000014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056331714
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1088/0266-5611/6/4/005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041998952
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1088/1741-2560/8/2/025008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029194097
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1109/42.759120 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061170758
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1109/bsn.2010.52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094956752
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1109/embc.2013.6610665 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078797400
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1109/iembs.2007.4353650 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077517882
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1109/iembs.2008.4650549 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077840038
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1109/iembs.2011.6090895 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078503035
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1109/iscas.2008.4541835 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093847048
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1109/tbme.1969.4502613 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061523547
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1109/tbme.2014.2347318 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061529650
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1109/tmi.2004.837363 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061694650
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1155/2011/923703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036020403
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1523/jneurosci.0552-13.2014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037611035
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1523/jneurosci.1091-05.2005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015109357
176 rdf:type schema:CreativeWork
177 https://doi.org/10.21014/acta_imeko.v3i3.94 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068830589
178 rdf:type schema:CreativeWork
179 https://doi.org/10.3389/fneur.2012.00114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015152662
180 rdf:type schema:CreativeWork
181 https://doi.org/10.3389/fnhum.2014.00182 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015323219
182 rdf:type schema:CreativeWork
183 https://doi.org/10.3389/fnins.2011.00053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009258039
184 rdf:type schema:CreativeWork
185 https://doi.org/10.3389/fnins.2012.00060 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032048425
186 rdf:type schema:CreativeWork
187 https://doi.org/10.3390/machines2010087 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028484132
188 rdf:type schema:CreativeWork
189 https://doi.org/10.3390/s140712847 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032678446
190 rdf:type schema:CreativeWork
191 https://doi.org/10.4028/www.scientific.net/ast.96.102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072049487
192 rdf:type schema:CreativeWork
 




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


...