## 4 Discussion
The literature on the effect of the extensive exposure or practice variable on EEG correlates of emotional processing is scant (for example see [72]) but there is evidence for the n-Back task that Alpha and Theta increase with cognitive load [73; 74; 75]. Our findings show that both EPOC and Brainvision did not find Alpha desynchronization followed by a concurrent increase of slow frequency oscillations (Deltaor Theta), as reported by some abovementioned literature. In our study, Alpha showed occasional increases or null findings while increases in Delta were rather ubiquitous and inconsistent. The most frequent and consistent changes were increases in Theta, Beta and Gamma going from low to high mental states, which is, as we have cursorily mentioned, in keeping with the literature on practiced mental or emotional processing load. This is both interesting as an extension of research on training of cognitive load and working memory capacity as well as, we believe, a relatively new contribution for the area on emotional processing since there are very few event-related EEG studies on repeated exposure to pleasant or unpleasant stimuli. In our experiment the sample size was very low, and it was impossible to achieve reliable results that generalize to population distributions. However, because we used an ideographic approach (i.e., electrode-wise analysis of vincentized data) which we can defend to be generalized at the level of person prediction, the actual sample size was relatively unimportant for the explicitly stated scope (tests of reliability) addressed in this paper; this last point holds especially because we took several convergent repeated measurements (N = 14), which were the actual basis for our test of accuracy. Nonetheless, a possible caveat is that, given our very small sample, it was obviously impossible to properly counterbalance learning effects associated with the device used, namely, EPOC was used first and then Brainvision was used on all subjects, potentially, this order might have induced a learning effect favoring Brainvision. Yet, the rate of significant differences between high and low mental states picked up by EPOC and Brainvision did not show any differential order trend, that is, there were no obvious and consistent differences between sensitivity in detecting significant effects between mental states for the first device used or for the second one, and no advantage of one device over the other can be clearly observed or inferred. Another related possible caveat might be learning effects of the individual, regardless of the device used. However, as we have pointed out all three participants were graduate student professional observers which were abundantly familiar with stimuli, tasks, and devices. Furthermore, all participants carried out the tasks in a different order. Most likely, these two aspects were sufficient to wash out learning effects due to the task sequence during the experiment. Our findings would suggest that future applications, such as brain-computer interfaces, requiring a more ecological setup should be able to confidently use EPOC for tasks intending to elicit change in valence/arousal/mental load such as ANEW, IAPS, or n-Back. However, in a laboratory or medical setting the researcher or clinician would still benefit from the quality performance found using Brainvision. Future research should consider ways of optimizing the signal quality of the EPOC so as to obtain larger effect sizes when investigating differences of amplitude/power in response to different mental states. For instance, it would be feasible to leverage machine learning models to enhance the accuracy and classification capabilities of the signals obtained via the EPOC [29]. This would be a promising, although more complex, avenue for increasing the efficacy of the Emotiv device. Modifying the hardware of the EPOC may also be feasible and effective, as [PERSON] et al.