Principle ERP Reduction and Analysis: Estimating and Using Principle ERP Waveforms Underlying ERPs Across Tasks, Subjects and Electrodes

• Activity Number: 240 - Statistical Analysis of Complex Imaging Data
• Type: Invited
• Date/Time: Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
• Sponsor: WNAR
• Author(s): Damla Senturk* and Shafali Jeste and Emilie Campos and Chad Hazlett and Patricia Tan and Holly Truong and Sandra Loo and Charlotte DiStefano
• Companies: UCLA and UCLA and UCLA and UCLA and UCLA and UCLA and UCLA and UCLA
• Keywords: Electroencephalogram; ICA; Event-related potential ; principal components analysis; Autism Spectrum Disorder (ASD); Attention Deficit Hyperactivity Disorder (ADHD)

Abstract

Event-related potentials (ERP) waveforms are the summation of many overlapping signals. Changes in the peak or mean amplitude of a waveform over a given time period, therefore, cannot reliably be attributed to a particular ERP component of ex ante interest, as is the standard approach to ERP analysis. Though this problem is widely recognized, it is not well addressed in practice. Our approach begins by presuming that any observed ERP waveform — at any electrode, for any trial type, and for any participant — is approximately a weighted combination of signals from an underlying set of what we refer to as principle ERPs, or pERPs. We propose an accessible approach to analyzing complete ERP waveforms in terms of their underlying pERPs. The principle ERP reduction (pERP-RED) algorithm is proposed for investigators to estimate a suitable set of pERPs from their data, which may span multiple tasks. We demonstrate this suite of tools through simulations and on real data collected from multiple experiments on participants diagnosed with Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder. Software for conducting these analyses is provided in the pERPred package for R.