Publikation: Methods for Multivariate Time-Series Classification on Brain Data : Aggregation, Stratification and Neural Network Models
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
This thesis is about the analysis of two data sets consisting of human brain data measured by electroencephalography (EEG). One data set contains data from craving smokers, who have not smoked for several hours, non-craving smokers, who had a smoke shortly before the measurement and non-smokers. These classes are to be distinguished with the help of neural networks. The second data set contains noisy EEG signals with different kinds of noise from and clean signal that are to be distinguished. In order to analyze them, I adapt a network structure, that was originally developed for neural networks for object recognition in images. I modify, the so called residual blocks in order to use them on EEG time series. One difficulty of EEG data is their property of being individual-specific. This can sometimes even be helpful to get improved predictions: if the classes of the data change so infrequently that it can be assumed that several parts (so called snippets) of a longer signal belong to the same class, then this information can be used to make predictions of several snippets and aggregate them to create a classification of the longer original signal.
I investigate a total of 15 research questions, regarding the context in neuroscience, adaptations and improvements of neural network models, and the optimal choice of aggregation functions.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
DOELL, Christoph, 2021. Methods for Multivariate Time-Series Classification on Brain Data : Aggregation, Stratification and Neural Network Models [Dissertation]. Konstanz: University of KonstanzBibTex
@phdthesis{Doell2021Metho-53422, year={2021}, title={Methods for Multivariate Time-Series Classification on Brain Data : Aggregation, Stratification and Neural Network Models}, author={Doell, Christoph}, address={Konstanz}, school={Universität Konstanz} }
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/123456789/53422"> <dc:creator>Doell, Christoph</dc:creator> <dcterms:issued>2021</dcterms:issued> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/53422"/> <dcterms:title>Methods for Multivariate Time-Series Classification on Brain Data : Aggregation, Stratification and Neural Network Models</dcterms:title> <dc:language>eng</dc:language> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-04-21T06:20:39Z</dc:date> <dcterms:abstract xml:lang="eng">This thesis is about the analysis of two data sets consisting of human brain data measured by electroencephalography (EEG). One data set contains data from craving smokers, who have not smoked for several hours, non-craving smokers, who had a smoke shortly before the measurement and non-smokers. These classes are to be distinguished with the help of neural networks. The second data set contains noisy EEG signals with different kinds of noise from and clean signal that are to be distinguished. In order to analyze them, I adapt a network structure, that was originally developed for neural networks for object recognition in images. I modify, the so called residual blocks in order to use them on EEG time series. One difficulty of EEG data is their property of being individual-specific. This can sometimes even be helpful to get improved predictions: if the classes of the data change so infrequently that it can be assumed that several parts (so called snippets) of a longer signal belong to the same class, then this information can be used to make predictions of several snippets and aggregate them to create a classification of the longer original signal.<br />I investigate a total of 15 research questions, regarding the context in neuroscience, adaptations and improvements of neural network models, and the optimal choice of aggregation functions.</dcterms:abstract> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/53422/3/Doell_2-1dhjtzl2q3lt61.pdf"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-04-21T06:20:39Z</dcterms:available> <dc:rights>Attribution 4.0 International</dc:rights> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/> <dc:contributor>Doell, Christoph</dc:contributor> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/53422/3/Doell_2-1dhjtzl2q3lt61.pdf"/> </rdf:Description> </rdf:RDF>