1.0 Introduction
For over seven decades, electroencephalography (EEG) has been used to study brain activity elicited by the presentation of sensory stimuli, or during performance of motor or cognitive tasks (Kappenman & Luck, 2016). A cornerstone of the general methodology used to obtain measures of brain activity has been the processing step of averaging multiple segments of EEG signals elicited by the same stimulus to create averaged event-related potentials (ERPs). Then, the resulting peaks and valleys of the averaged ERP are quantified for a specified time window as either: 1) the average of the voltage values of the data points in the window or 2) as the amplitude and latency of the largest peak found in the time window.
Justification for the processing step of averaging multiple segments is based on three assumptions. Firstly, EEG obtained at any scalp site is a continuous mixture of electrical activity from multiple brain regions, where some of the activity is not related to the stimulus. This unrelated activity is often referred to as background (brain) noise. Secondly, the brain’s electrical signal elicited by any stimulus (or response) event is difficult to measure as it is assumed to be smaller than, and mixed with, the continuous background noise. Thirdly, the brain’s signal in response to an event is invariant in amplitude and time for each occurrence of the event (Dawson, 1954; Hu et al., 2011; Quian Quiroga & Garcia, 2003). While the validity of the second and third assumptions have received considerable discussion over the history of ERP-based research, some researchers propose that the best approach to address these assumptions is using the average voltage in a window measurement (e.g., De Vos, Thorne, Yovel, & Debener, 2012; Jongsma et al., 2006; Kappenman & Luck, 2016; Kosciessa, Grandy, Garrett, & Werkle-Bergner, 2020; Luck, Stewart, Simmons, Rhemtulla., 2021). Yet even this measurement approach relies on the third assumption, signal invariance, being true or at least having minimal variations in the latencies of the various positive and negative peaks across segments being averaged. While latency variability is often minimal for adults, research in children has shown that greater latency variability of peaks leads to decreased amplitude measures, which in turn leads to erroneous conclusions about brain development (e.g., DuPuis et al., 2015; Gavin, Lin, & Davies, 2019).
The present study investigates the feasibility of using a simple procedure, the Single Trial Peak approach (STP), for obtaining reliable and valid peak amplitude and latency measures at the trial (i.e., segment) level. This procedure does not depend on the validity of the second and third assumptions traditionally presumed in research using averaged ERPs; that is, it does not require a signal-to-noise ratio (SNR) of any value, nor does it assume signal invariance. In contrast, the STP procedure does assume, allows for, and quantifies not only the variability between participants representing individual differences in processing abilities but also the variability across trials within a paradigm/task completed by the participant. Consequently, the STP procedure enables researchers to test the hypothesis that some of the variability in amplitude and latency of peaks across trials may be systematic rather than random. More importantly, the STP procedure can produce measures of brain activity at the single trial level with psychometric properties comparable to or exceeding the commonly used measure of average voltage in defined time windows. The rationale for proposing the STP procedure is presented in the following sections.