title: Novel machine learning methods for ERP analysis: a validation from research on infants at risk for autism.
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.
authors.affiliation: Department of Biostatistics, Institute of Psychiatry, King's College London, London, United Kingdom. daniel.r.stahl@kcl.ac.uk
authors.forename: Daniel
authors.initials: D
authors.lastname: Stahl
authors.forename: Andrew
authors.initials: A
authors.lastname: Pickles
authors.forename: Mayada
authors.initials: M
authors.lastname: Elsabbagh
authors.forename: Mark H
authors.initials: MH
authors.lastname: Johnson
mesh_terms: D001185:Artificial Intelligence; D001288:Attention / Q000502:physiology; D001321:Autistic Disorder / Q000175:diagnosis* / Q000503:physiopathology*; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods; D005071:Evoked Potentials / Q000502:physiology*; D005403:Fixation, Ocular; D006801:Humans; D007223:Infant; D016015:Logistic Models; D010363:Pattern Recognition, Automated; D011930:Reaction Time / Q000502:physiology; D015203:Reproducibility of Results; D012306:Risk