## 2 Materials and Methods  
### Sample and Data Re-Analysis Approach  
The data utilized in this study was derived from a previous study by the same authors [20] which investigated global averages of EEG power across the scalp comparing EEG signal pooled across all electrodes (14 for EPOC vs. 32 for Brainvision). The database used repeated long recording session samples from \"professional observers\" in the span of 48 hours. Participants were three healthy university graduate students (mean age 24.66 years old). They all gave verbal consent to participate and waived the requirement of signed consent. The study was part of a grant funded pilot project which was retrospectively assessed and approved by Carleton University Institutional Research Ethics Board (section Human Subjects) under the regulation set by the Canadian Tri-Council [58]. All participants were right-handed males with corrected-to-normal or normal vision, and none reported any history of neurological impairment or were currently using psychoactive medications. The average number of hours of sleep on the previous night was 7.5 h. Testing was conducted from 10 a.m. to 4 p.m. with a one-hour break in between on two separate days. The pre-experimental short adult version of the _mood and feelings questionnaire_[5] was administered by an independent research assistant unaware of the hypotheses and goals of the study to screen for mood differences or emotional changes before the experimental sessions between days. No remarkable differences were reported as all participants scored similarly (overall score range: 3-5) in the two days and consistently well below the recommended clinical cut-off (possible maximum = 26; clinical cut-off \(\geq\) 11). Prior to the experiment, all participants gained familiarity with the actual stimuli and conditions of the tasks during several study sessions, since they were involved in selecting the stimuli and the conditions, they designed the computer programs for the tasks, and they test piloted the delivery and the performance of the task computer programs and the instrumentation. Since all these activities involved extensive exposure and practice over time, it can be assumed the materials of the tasks were overlearned and practice effects, particularly due to the order in which the devices were used, were washed away. This compensated for lack of counterbalancing due to small sample. The present study utilized a different analytical strategy going beyond global EEG patterns, to focus specifically on the accuracy and reliability of different EEG signals at electrode sites matched between the two devices; these present analyses and results are novel unpublished findings. Thus, the unit of analysis, or \"subjects\" were the 14 EEG electrodes. This type of within-subject approach is usually traditionally known as by-item or item-wise analysis [42; 59] or, in keeping with EEG terminology, an _electrode-wise_ analysis. The item-wise approach corresponds to a random effects model on the items, and a fixed-effects model on subjects. Therefore, the effects from statistical tests can be generalized to new items and task from the same subjects but cannot be used to make a reliable prediction for new subjects that would be generalized to the population [60]. The mean-wise group approach is ordinarily used to obtain the highest possible effect. In contrast, the item-wise approach centers around the pursuit of replicable effects based on weak but stable interindividual correlation coefficients, that is, on relatively homogeneous intraindividual variance. The weak correlation for an item is generally due to excessive interindividual noise. To reduce such noise heterogeneity, in our study the participants' data were first vincentized, that is, they were partitioned according to time-series bins with same data density per interval of time by averaging the participants' quantile functions, as previously done by [PERSON] et al. (2020) [61]. Successively, bin-by-bin means were estimated for each electrode (i.e., across participants).