PUBLICATION DATE

2012

TITLE

Novel machine learning methods for ERP analysis: a validation from research on infants at risk for autism.

ISSUE

3

VOLUME

37

ISSN (print)

N/A

ISSN (electronic)

N/A

ABSTRACT

Machine learning and other computer intensive pattern recognition methods are successfully applied to a variety of fields that deal with high-dimensional data and often small sample sizes such as genetic microarray, functional magnetic resonance imaging (fMRI) and, more recently, electroencephalogram (EEG) data. The aim of this article is to discuss the use of machine learning and discrimination methods and their possible application to the analysis of infant event-related potential (ERP) data. The usefulness of two methods, regularized discriminant function analyses and support vector machines, will be demonstrated by reanalyzing an ERP dataset from infants ( Elsabbagh et al., 2009 ). Using cross-validation, both methods successfully discriminated above chance between groups of infants at high and low risk of a later diagnosis of autism. The suitability of machine learning methods for the use of single trial or averaged ERP data is discussed.

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JOURNAL BRAND

N/A (note: articles not published by Springer Nature have limited metadata)


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  • The Typical And Atypical Development Of The Social Brain During Infancy
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    13 TRIPLES      13 PREDICATES      14 URIs      9 LITERALS

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    1 articles:78f409162355a091a3c46fbbe841e4dd sg:abstract Machine learning and other computer intensive pattern recognition methods are successfully applied to a variety of fields that deal with high-dimensional data and often small sample sizes such as genetic microarray, functional magnetic resonance imaging (fMRI) and, more recently, electroencephalogram (EEG) data. The aim of this article is to discuss the use of machine learning and discrimination methods and their possible application to the analysis of infant event-related potential (ERP) data. The usefulness of two methods, regularized discriminant function analyses and support vector machines, will be demonstrated by reanalyzing an ERP dataset from infants ( Elsabbagh et al., 2009 ). Using cross-validation, both methods successfully discriminated above chance between groups of infants at high and low risk of a later diagnosis of autism. The suitability of machine learning methods for the use of single trial or averaged ERP data is discussed.
    2 sg:doi 10.1080/87565641.2011.650808
    3 sg:doiLink http://dx.doi.org/10.1080/87565641.2011.650808
    4 sg:isFundedPublicationOf grants:a0e1a5e68fab70d78f85be7a89a72678
    5 sg:issue 3
    6 sg:language English
    7 sg:license http://scigraph.springernature.com/explorer/license/
    8 sg:publicationYear 2012
    9 sg:scigraphId 78f409162355a091a3c46fbbe841e4dd
    10 sg:title Novel machine learning methods for ERP analysis: a validation from research on infants at risk for autism.
    11 sg:volume 37
    12 rdf:type sg:Article
    13 rdfs:label Article: Novel machine learning methods for ERP analysis: a validation from research on infants at risk for autism.
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