1.2 Justification for an Alternate Method for Quantifying Single Trial Measurements: A Peak-Picking Approach
Peak picking is defined as the identification of the largest positive or negative peak amplitude in the averaged ERP in a pre-determined time window (Weeda et al., 2012; Zhang & Luo, 2011). The time window used to define a given ERP component can be based on time windows used in the extant literature. If prior studies using similar stimulus paradigms are unavailable, the morphology depicted in the grand averaged ERP (i.e., the average of all averaged ERPs of all participants in the study) can be used to determine a time window that captures the component of interest for the peak picking of the averaged ERPs of each individual participant. The advantages of the peak-picking methodology are that it allows one to 1) obtain an amplitude and latency measure of the component of interest at its maximum point, 2) measure differences in latency between individual participants or the conditions in the study, and 3) attain a greater sensitivity (i.e., lower measurement error) in the peak amplitude measurements when compared to averaging voltages in the window. Additionally, if each major peak of an averaged ERP is measured, then the obtained amplitude and latency measures define a generalized morphology of brain processing to the event for each participant.
Once a peak is identified in the peak-picking approach described above, there are two ways that the amplitude can be calculated. One approach to peak picking has been to measure peak amplitude from the baseline (a.k.a. baseline-to-peak measures) after performing the processing step of baseline correction. While baseline correction aids in normalizing the segmented trial data to changes in voltage from a zero-voltage referent, this procedural step can also make the peak sensitive to background noise, especially in single trials (Weeda et al., 2012). The second approach, measuring amplitudes from peak-to-peak may be less sensitive to baseline noise and, therefore, a better way to measure amplitudes with single trials (Ribeiro, Paiva, & Castelo-Branco, 2016; Unsal & Segalowitz, 1995). However, it has yet to be identified how peak-picking single trials compares with an averaged voltage within a time window.
The ST approach used in this study to identify the principal peaks in a single trial is based on the common method of peak picking to identify component peaks and latencies in the averaged ERP of each individual participant in a study. When developing the STP approach, peak picking at trial level was viewed as a logical extension of peak picking at the averaged ERP level. That is, any given peak identified in the averaged ERP is a product of the peaks in the single trials occurring on or near the time point (latency) of the averaged ERP component of interest. Furthermore, when the general morphology of the averaged ERP is captured as successive measures of the major components (e.g., the N1, P2, N2, P3, N3 peaks), the time window for finding the ST peaks of a component of interest is defined by the time points of the two surrounding components in the averaged ERP (i.e., the latencies of the preceding component and following component). For example, to obtain a measurement of the P2 for a single trial one can determine the maximum voltage between the latencies of the N1 and the N2 components of the averaged ERP. The latency for this maximum voltage in the single trial will most likely be close to the latency of the P2 of the averaged ERP but will vary from trial-to-trial. The use of the prior and post latencies for determining a peak at the ST level is logical, as every data point in the single trial contributes to the general morphology of the data points in the averaged ERP.