Robust functional clustering of ERP data with application to a study of implicit learning in autism.

ABSTRACT: Motivated by a study on visual implicit learning in young children with Autism Spectrum Disorder (ASD), we propose a robust functional clustering (RFC) algorithm to identify subgroups within electroencephalography (EEG) data. The proposed RFC is an iterative algorithm based on functional principal component analysis, where cluster membership is updated via predictions of the functional trajectories obtained through a non-parametric random effects model. We consider functional data resulting from event-related potential (ERP) waveforms representing EEG time-locked to stimuli over the course of an implicit learning experiment, after applying a previously proposed meta-preprocessing step. This meta-preprocessing is designed to increase the low signal-to-noise ratio in the raw data and to miti

SUBMITTER: Hasenstab K 

PROVIDER: S-EPMC4915609 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature