Abstract Details
Activity Number:
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470
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Type:
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Invited
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Date/Time:
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Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #314245
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Title:
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Identifying Longitudinal Trends Within EEG Experiments
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Author(s):
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Damla Senturk* and Kyle Hasenstab and Donatello Telesca and Catherine Sugar and Shafali Jeste
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Companies:
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UCLA and UCLA and UCLA and UCLA and UCLA
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Keywords:
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event-related potentials data ;
heteroskedasticity ;
repeated measurements ;
signal-to-noise ratio ;
smoothing ;
weighted linear mixed effects models
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Abstract:
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Differential brain response to sensory stimuli is very small (a few microvolts) compared to the overall magnitude of spontaneous electroencephalographam (EEG), yielding a low signal-to-noise ratio (SNR) in studies of event-related potentials (ERP). To cope with this phenomenon, stimuli are applied repeatedly and the ERP signals arising from the individual trials are averaged at the subject level. This results in the loss of information about potentially important changes in the magnitude and form of ERP signals over the course of the experiment. In this paper, we develop a meta-preprocessing step for ERP studies to capture such longitudinal trends in ERP data. The proposed method utilizes a moving average of ERP across sliding trial windows without imposing parametric modeling assumptions. We embed this procedure in a weighted linear mixed effects model to describe longitudinal trends in features such as ERP peak amplitude and latency across trials while adjusting for the inherent heteroskedasticity created at the meta-preprocessing step. The proposed unified framework including the meta-processing and the weighted linear mixed effects modeling steps is referred to as the MAP-ERP
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Authors who are presenting talks have a * after their name.
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