Publikation:

Residual Neural Networks to Distinguish Craving Smokers, Non-craving Smokers and Non-smokers by their EEG signals

Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2018

Autor:innen

Donohue, Sarah E.

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
DOI (zitierfähiger Link)
https://doi.org/10.1109/SSCI.2018.8628678
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung

Sammlungen

Informatik und Informationswissenschaft: Publikationen
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

SUNDARAM, Suresh, ed.. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE, 2018, pp. 510-517. ISBN 978-1-5386-9276-9. Available under: doi: 10.1109/SSCI.2018.8628678

Zusammenfassung

We investigate the differences in brain signals of craving smokers, non-craving smokers, and non-smokers. To this end, we use data from resting-state EEG measurements to train predictive models to distinguish these three groups. We improve the neural network models applied earlier in two ways: firstly by adding channel-wise convolutional layers, secondly by adding residual connections to the network. We further extend the validation to make it similar to a real world scenario, in which a prediction is based on all data available for this measurement. Finally, we analyze the prediction quality for each measurement individually. Our results demonstrate significant improvements.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Addiction, Smoker, Craving, Residual Neural Network, EEG, Classification

Konferenz

2018 IEEE Symposium Series on Computational Intelligence (SSCI), 18. Nov. 2018 - 21. Nov. 2018, Bangalore, India
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690DOELL, Christoph, Sarah E. DONOHUE, Christian BORGELT, 2018. Residual Neural Networks to Distinguish Craving Smokers, Non-craving Smokers and Non-smokers by their EEG signals. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). Bangalore, India, 18. Nov. 2018 - 21. Nov. 2018. In: SUNDARAM, Suresh, ed.. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE, 2018, pp. 510-517. ISBN 978-1-5386-9276-9. Available under: doi: 10.1109/SSCI.2018.8628678
BibTex
@inproceedings{Doell2018Resid-45354,
  year={2018},
  doi={10.1109/SSCI.2018.8628678},
  title={Residual Neural Networks to Distinguish Craving Smokers, Non-craving Smokers and Non-smokers by their EEG signals},
  isbn={978-1-5386-9276-9},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI)},
  pages={510--517},
  editor={Sundaram, Suresh},
  author={Doell, Christoph and Donohue, Sarah E. and Borgelt, Christian}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/45354">
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Doell, Christoph</dc:creator>
    <dc:creator>Borgelt, Christian</dc:creator>
    <dc:contributor>Borgelt, Christian</dc:contributor>
    <dcterms:title>Residual Neural Networks to Distinguish Craving Smokers, Non-craving Smokers and Non-smokers by their EEG signals</dcterms:title>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Donohue, Sarah E.</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:abstract xml:lang="eng">We investigate the differences in brain signals of craving smokers, non-craving smokers, and non-smokers. To this end, we use data from resting-state EEG measurements to train predictive models to distinguish these three groups. We improve the neural network models applied earlier in two ways: firstly by adding channel-wise convolutional layers, secondly by adding residual connections to the network. We further extend the validation to make it similar to a real world scenario, in which a prediction is based on all data available for this measurement. Finally, we analyze the prediction quality for each measurement individually. Our results demonstrate significant improvements.</dcterms:abstract>
    <dc:contributor>Donohue, Sarah E.</dc:contributor>
    <dcterms:issued>2018</dcterms:issued>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-03-07T13:16:50Z</dc:date>
    <dc:language>eng</dc:language>
    <dc:contributor>Doell, Christoph</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/45354"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-03-07T13:16:50Z</dcterms:available>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen