Literature DB >> 16372388

Functional ANOVA with random functional effects: an application to event-related potentials modelling for electroencephalograms analysis.

Céline Bugli1, Philippe Lambert.   

Abstract

The differential effects of basic visual or auditory stimuli on electroencephalograms (EEG), named event related potentials (ERPs), are often used to evaluate the impact of treatments on brain performances. In the present paper, we propose a P-splines based model that can be used to evaluate treatment effect on the timing and the amplitude of some peaks of the ERPs curves. Functional ANOVA is an adaptation of linear model or analysis of variance to analyse functional observations. The changes in the functional of interest effects are generally described using smoothing splines. Eilers and Marx proposed to work with P-splines, a combination of B-splines and difference penalties on coefficients. We define a P-splines model for ERPs curves combined with random effects. In particular, we show that it is a useful alternative to classical strategies requiring the visual and usually imprecise localization of specific ERP peaks from curves with a low signal-to-noise ratio.

Mesh:

Year:  2006        PMID: 16372388     DOI: 10.1002/sim.2464

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  3 in total

1.  A multi-dimensional functional principal components analysis of EEG data.

Authors:  Kyle Hasenstab; Aaron Scheffler; Donatello Telesca; Catherine A Sugar; Shafali Jeste; Charlotte DiStefano; Damla Şentürk
Journal:  Biometrics       Date:  2017-01-10       Impact factor: 2.571

2.  A study of longitudinal trends in time-frequency transformations of EEG data during a learning experiment.

Authors:  Joanna Boland; Donatello Telesca; Catherine Sugar; Shafali Jeste; Cameron Goldbeck; Damla Senturk
Journal:  Comput Stat Data Anal       Date:  2021-10-08       Impact factor: 2.035

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

Authors:  Kyle Hasenstab; Catherine Sugar; Donatello Telesca; Shafali Jeste; Damla Şentürk
Journal:  Biostatistics       Date:  2016-02-04       Impact factor: 5.899

  3 in total

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