Document Detail

Novel machine learning methods for ERP analysis: a validation from research on infants at risk for autism.
MedLine Citation:
PMID:  22545662     Owner:  NLM     Status:  MEDLINE    
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.
Daniel Stahl; Andrew Pickles; Mayada Elsabbagh; Mark H Johnson;
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Developmental neuropsychology     Volume:  37     ISSN:  1532-6942     ISO Abbreviation:  Dev Neuropsychol     Publication Date:  2012  
Date Detail:
Created Date:  2012-05-01     Completed Date:  2012-09-04     Revised Date:  2014-02-20    
Medline Journal Info:
Nlm Unique ID:  8702038     Medline TA:  Dev Neuropsychol     Country:  England    
Other Details:
Languages:  eng     Pagination:  274-98     Citation Subset:  IM    
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MeSH Terms
Artificial Intelligence*
Attention / physiology
Autistic Disorder / diagnosis*,  physiopathology*
Discriminant Analysis*
Electroencephalography / methods
Evoked Potentials / physiology*
Fixation, Ocular
Logistic Models
Pattern Recognition, Automated
Reaction Time / physiology
Reproducibility of Results
Grant Support
G0701484//Medical Research Council; PG0701484//Medical Research Council
Simon Baron-Cohen / ; Patrick Bolton / ; Tony Charman / ; Holly Garwood / ; Karla Holmboe / ; Leslie Tucker / ; Agnes Volein /

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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