Statistical Detection of Movement Activities in a Human Brain by Moving Separation of Mixture Distributions* View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-10

AUTHORS

A. K. Gorshenin, V. Yu. Korolev, A. Yu. Korchagin, T. V. Zakharova, A. I. Zeifman

ABSTRACT

One of the most popular experimental techniques for investigation of brain activity is the so-called method of evoked potentials: the subject repeatedly makes some movements (by his/her finger), whereas brain activity and some auxiliary signals are recorded for further analysis. The key problem is the detection of points in the myogram that correspond to the beginning of the movements. The more precisely the points are detected, the more successfully the magnetoencephalogram is processed aiming at the identification of sensors that are closest to the activity areas. This paper proposes a statistical approach to this problem based on mixtures models that uses a specially modified method of moving separation of mixtures of probability distributions (MSMmethod) to detect the start points of the finger’s movements. We demonstrate the correctness of the new procedure and its advantages as compared with the method based on the notion of the myogram window variance. More... »

PAGES

278-286

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10958-016-3029-1

DOI

http://dx.doi.org/10.1007/s10958-016-3029-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020357519


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/2008/1109", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Neurosciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Moscow Technological University", 
          "id": "https://www.grid.ac/institutes/grid.466477.0", 
          "name": [
            "Institute of Informatics Problems of Federal Research Center \u201cInformatics and Control\u201d, Russian Academy of Sciences, Moscow, Russia", 
            "Federal State Budget Educational Institution of Higher Education \u201cMoscow Technological University\u201d, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gorshenin", 
        "givenName": "A. K.", 
        "id": "sg:person.011450150153.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011450150153.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Russian Academy of Sciences", 
          "id": "https://www.grid.ac/institutes/grid.4886.2", 
          "name": [
            "Faculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia", 
            "Institute of Informatics Problems of Federal Research Center \u201cInformatics and Control\u201d, Russian Academy of Sciences, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Korolev", 
        "givenName": "V. Yu.", 
        "id": "sg:person.014166423003.73", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014166423003.73"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Moscow State University", 
          "id": "https://www.grid.ac/institutes/grid.14476.30", 
          "name": [
            "Faculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Korchagin", 
        "givenName": "A. Yu.", 
        "id": "sg:person.010170417462.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010170417462.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Moscow State University", 
          "id": "https://www.grid.ac/institutes/grid.14476.30", 
          "name": [
            "Faculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zakharova", 
        "givenName": "T. V.", 
        "id": "sg:person.014470730077.60", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014470730077.60"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Vologda State Technical University", 
          "id": "https://www.grid.ac/institutes/grid.445065.6", 
          "name": [
            "Institute of Informatics Problems of Federal Research Center \u201cInformatics and Control\u201d, Russian Academy of Sciences, Moscow, Russia", 
            "Vologda State University, Vologda, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zeifman", 
        "givenName": "A. I.", 
        "id": "sg:person.016403133727.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016403133727.49"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-1-4612-5698-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014833716", 
          "https://doi.org/10.1007/978-1-4612-5698-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4612-5698-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014833716", 
          "https://doi.org/10.1007/978-1-4612-5698-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9780511546396.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052363950"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aoms/1177704481", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064400518"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.14357/08696527140107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067273897"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4213/tvp4496", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072377086"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-10", 
    "datePublishedReg": "2016-10-01", 
    "description": "One of the most popular experimental techniques for investigation of brain activity is the so-called method of evoked potentials: the subject repeatedly makes some movements (by his/her finger), whereas brain activity and some auxiliary signals are recorded for further analysis. The key problem is the detection of points in the myogram that correspond to the beginning of the movements. The more precisely the points are detected, the more successfully the magnetoencephalogram is processed aiming at the identification of sensors that are closest to the activity areas. This paper proposes a statistical approach to this problem based on mixtures models that uses a specially modified method of moving separation of mixtures of probability distributions (MSMmethod) to detect the start points of the finger\u2019s movements. We demonstrate the correctness of the new procedure and its advantages as compared with the method based on the notion of the myogram window variance.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10958-016-3029-1", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1136516", 
        "issn": [
          "1072-3374", 
          "1573-8795"
        ], 
        "name": "Journal of Mathematical Sciences", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "218"
      }
    ], 
    "name": "Statistical Detection of Movement Activities in a Human Brain by Moving Separation of Mixture Distributions*", 
    "pagination": "278-286", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "60ff7692a9536ea352fbd0b46a8113fa9724beb1fe2aa9b721cd3b7001037492"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10958-016-3029-1"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1020357519"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10958-016-3029-1", 
      "https://app.dimensions.ai/details/publication/pub.1020357519"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:21", 
    "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/v90/0000000362_0000000362/records_87079_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10958-016-3029-1"
  }
]
 

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/s10958-016-3029-1'

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/s10958-016-3029-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10958-016-3029-1'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10958-016-3029-1'


 

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

117 TRIPLES      21 PREDICATES      32 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10958-016-3029-1 schema:about anzsrc-for:11
2 anzsrc-for:1109
3 schema:author N2c7b8c8169564f96aba5526df2053b78
4 schema:citation sg:pub.10.1007/978-1-4612-5698-4
5 https://doi.org/10.1017/cbo9780511546396.004
6 https://doi.org/10.1214/aoms/1177704481
7 https://doi.org/10.14357/08696527140107
8 https://doi.org/10.4213/tvp4496
9 schema:datePublished 2016-10
10 schema:datePublishedReg 2016-10-01
11 schema:description One of the most popular experimental techniques for investigation of brain activity is the so-called method of evoked potentials: the subject repeatedly makes some movements (by his/her finger), whereas brain activity and some auxiliary signals are recorded for further analysis. The key problem is the detection of points in the myogram that correspond to the beginning of the movements. The more precisely the points are detected, the more successfully the magnetoencephalogram is processed aiming at the identification of sensors that are closest to the activity areas. This paper proposes a statistical approach to this problem based on mixtures models that uses a specially modified method of moving separation of mixtures of probability distributions (MSMmethod) to detect the start points of the finger’s movements. We demonstrate the correctness of the new procedure and its advantages as compared with the method based on the notion of the myogram window variance.
12 schema:genre research_article
13 schema:inLanguage en
14 schema:isAccessibleForFree true
15 schema:isPartOf N4d70a7fc4c0b4e4c89c0802665029b57
16 N89cba6edd08d4e3b8fd1361559a9450f
17 sg:journal.1136516
18 schema:name Statistical Detection of Movement Activities in a Human Brain by Moving Separation of Mixture Distributions*
19 schema:pagination 278-286
20 schema:productId N06149b37440b45099017714010647964
21 Nadf1a8180bd646cb98e89019f2a43bab
22 Ne0fd2519e0694b4ba707220c1c66a477
23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020357519
24 https://doi.org/10.1007/s10958-016-3029-1
25 schema:sdDatePublished 2019-04-11T12:21
26 schema:sdLicense https://scigraph.springernature.com/explorer/license/
27 schema:sdPublisher Ne9adbc0ae80245bbaacad5edef25559d
28 schema:url https://link.springer.com/10.1007%2Fs10958-016-3029-1
29 sgo:license sg:explorer/license/
30 sgo:sdDataset articles
31 rdf:type schema:ScholarlyArticle
32 N06149b37440b45099017714010647964 schema:name doi
33 schema:value 10.1007/s10958-016-3029-1
34 rdf:type schema:PropertyValue
35 N1daf9e565f844227b53f2a940dbae556 rdf:first sg:person.014166423003.73
36 rdf:rest Nd3b885ad9f53475986c6e34e67f9cc39
37 N2c7b8c8169564f96aba5526df2053b78 rdf:first sg:person.011450150153.25
38 rdf:rest N1daf9e565f844227b53f2a940dbae556
39 N4d70a7fc4c0b4e4c89c0802665029b57 schema:volumeNumber 218
40 rdf:type schema:PublicationVolume
41 N74965e6da10a472aa4a7d7683bd48929 rdf:first sg:person.014470730077.60
42 rdf:rest Ndb1a559c44d64017b90c2343f13dcb31
43 N89cba6edd08d4e3b8fd1361559a9450f schema:issueNumber 3
44 rdf:type schema:PublicationIssue
45 Nadf1a8180bd646cb98e89019f2a43bab schema:name readcube_id
46 schema:value 60ff7692a9536ea352fbd0b46a8113fa9724beb1fe2aa9b721cd3b7001037492
47 rdf:type schema:PropertyValue
48 Nd3b885ad9f53475986c6e34e67f9cc39 rdf:first sg:person.010170417462.26
49 rdf:rest N74965e6da10a472aa4a7d7683bd48929
50 Ndb1a559c44d64017b90c2343f13dcb31 rdf:first sg:person.016403133727.49
51 rdf:rest rdf:nil
52 Ne0fd2519e0694b4ba707220c1c66a477 schema:name dimensions_id
53 schema:value pub.1020357519
54 rdf:type schema:PropertyValue
55 Ne9adbc0ae80245bbaacad5edef25559d schema:name Springer Nature - SN SciGraph project
56 rdf:type schema:Organization
57 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
58 schema:name Medical and Health Sciences
59 rdf:type schema:DefinedTerm
60 anzsrc-for:1109 schema:inDefinedTermSet anzsrc-for:
61 schema:name Neurosciences
62 rdf:type schema:DefinedTerm
63 sg:journal.1136516 schema:issn 1072-3374
64 1573-8795
65 schema:name Journal of Mathematical Sciences
66 rdf:type schema:Periodical
67 sg:person.010170417462.26 schema:affiliation https://www.grid.ac/institutes/grid.14476.30
68 schema:familyName Korchagin
69 schema:givenName A. Yu.
70 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010170417462.26
71 rdf:type schema:Person
72 sg:person.011450150153.25 schema:affiliation https://www.grid.ac/institutes/grid.466477.0
73 schema:familyName Gorshenin
74 schema:givenName A. K.
75 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011450150153.25
76 rdf:type schema:Person
77 sg:person.014166423003.73 schema:affiliation https://www.grid.ac/institutes/grid.4886.2
78 schema:familyName Korolev
79 schema:givenName V. Yu.
80 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014166423003.73
81 rdf:type schema:Person
82 sg:person.014470730077.60 schema:affiliation https://www.grid.ac/institutes/grid.14476.30
83 schema:familyName Zakharova
84 schema:givenName T. V.
85 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014470730077.60
86 rdf:type schema:Person
87 sg:person.016403133727.49 schema:affiliation https://www.grid.ac/institutes/grid.445065.6
88 schema:familyName Zeifman
89 schema:givenName A. I.
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016403133727.49
91 rdf:type schema:Person
92 sg:pub.10.1007/978-1-4612-5698-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014833716
93 https://doi.org/10.1007/978-1-4612-5698-4
94 rdf:type schema:CreativeWork
95 https://doi.org/10.1017/cbo9780511546396.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052363950
96 rdf:type schema:CreativeWork
97 https://doi.org/10.1214/aoms/1177704481 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064400518
98 rdf:type schema:CreativeWork
99 https://doi.org/10.14357/08696527140107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067273897
100 rdf:type schema:CreativeWork
101 https://doi.org/10.4213/tvp4496 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072377086
102 rdf:type schema:CreativeWork
103 https://www.grid.ac/institutes/grid.14476.30 schema:alternateName Moscow State University
104 schema:name Faculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia
105 rdf:type schema:Organization
106 https://www.grid.ac/institutes/grid.445065.6 schema:alternateName Vologda State Technical University
107 schema:name Institute of Informatics Problems of Federal Research Center “Informatics and Control”, Russian Academy of Sciences, Moscow, Russia
108 Vologda State University, Vologda, Russia
109 rdf:type schema:Organization
110 https://www.grid.ac/institutes/grid.466477.0 schema:alternateName Moscow Technological University
111 schema:name Federal State Budget Educational Institution of Higher Education “Moscow Technological University”, Moscow, Russia
112 Institute of Informatics Problems of Federal Research Center “Informatics and Control”, Russian Academy of Sciences, Moscow, Russia
113 rdf:type schema:Organization
114 https://www.grid.ac/institutes/grid.4886.2 schema:alternateName Russian Academy of Sciences
115 schema:name Faculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia
116 Institute of Informatics Problems of Federal Research Center “Informatics and Control”, Russian Academy of Sciences, Moscow, Russia
117 rdf:type schema:Organization
 




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


...