Principle ERP reduction and analysis: Estimating and using principle ERP waveforms underlying ERPs across tasks, subjects and electrodes

• Published in: NeuroImage (Orlando, Fla.) Vol. 212; p. 116630
• Main Authors: Campos, Emilie, Hazlett, Chad, Tan, Patricia, Truong, Holly, Loo, Sandra, DiStefano, Charlotte, Jeste, Shafali, Şentürk, Damla
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.05.2020; Elsevier Limited; Elsevier
• Subjects: Algorithms; Attention Deficit Disorder with Hyperactivity - physiopathology; Attention deficit hyperactivity disorder; Attention deficit hyperactivity disorder (ADHD); Autism; Autism spectrum disorder (ASD); Autism Spectrum Disorder - physiopathology; Brain - physiology; Child; Child, Preschool; EEG; Electrodes; Electroencephalography - methods; ERP; Event-related potentials; Evoked Potentials - physiology; Experiments; Female; Humans; ICA; Male; Noise; PCA; Scalp; Signal Processing, Computer-Assisted; ERP; Attention deficit hyperactivity disorder (ADHD); Autism spectrum disorder (ASD); EEG; ICA; PCA
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.116630

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. First, we propose the principle ERP reduction (pERP-RED) algorithm for investigators to estimate a suitable set of pERPs from their data, which may span multiple tasks. Next, we provide tools and illustrations of pERP-space analysis, whereby observed ERPs are decomposed into the amplitudes of the contributing pERPs, which can be contrasted across conditions or groups to reveal which pERPs differ (substantively and/or significantly) between conditions/groups. Differences on all pERPs can be reported together rather than selectively, providing complete information on all components in the waveform, thereby avoiding selective reporting or user discretion regarding the choice of which components or windows to use. The scalp distribution of each pERP can also be plotted for any group/condition. 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. •We propose an approach to ERP analysis that avoids using peak or mean amplitude.•ERPs are assumed to be a weighted combination of underlying signals, called pERPs.•We propose an accessible approach to analyzing ERPs in terms of their pERPs.•We provide a set of tools, called ``pERP-space analysis'', for examining ERPs.•We also provide easy-to-use software to conduct these analyses in R.