## 4 Discussion  
The results of our study overall seem to suggest that EPOC is capable of differentiating between high vs low arousal/valence states using the amplitude of different frequency bands, but that the overall signal detected from EPOC was only a fraction of the signal detected by Brainvision. Despite the poorer signal detection, EPOC was still able to differentiate between the mental states via amplitude in most instances, although it had relatively small effect sizes compared to those of Brainvision. For amplitude, it also appears that EPOC most prominently detected differences in low frequency bands such as Delta and Theta. Brainvision on the other hand commonly detected differences of amplitude across all frequency bands. As above, the same can generally be said about the EPOC for differentiating mental states using EEG power, with a few exceptions. The ability of EPOC to detect Gamma power whatsoever was regularly quite low. Its mean amplitude was 8.7% of the amplitude measured by Brainvision. Even though EPOC was able to identify significant differences between high and low mental states in Gamma waves, the corresponding amplitude values never rose above 1.5 uV, even though Brainvision's measures went up to 13 uV. Since Gamma waves have the lowest amplitude of EEG frequency bands, the difficulty in registering their activity might be caused by the limited sensitivity and limited amount of electrodes on the EPOC headset. Additional factors could also include lower electrode accuracy and higher impedance. EPOC does not use conductive paste but saline solution, which can leave the electrodes more prone to corrosion and dry out more quickly than electrogel or paste. Unexpectedly, in the instance of the n-Back task 2-4-6 EPOC actually appeared to outperform Brainvision's capability of differentiating high vs low mental load across frequency bands. In this same way, the power detected via EPOC was greater than the power detected by Brainvision. However, Brainvision still performed well in differentiating mental load during the n-Back task with reasonable effect sizes. Given that the focus of the present study was accuracy and reliability, the tasks we used were selected because they demonstrated validity in distinguishing a relative difference in low and high psychological states as defined by subjective self-ratings of the participants and as reflected by concurrent state-dependent changes in EEG. Our study however has some implications and interesting insights also for research on comparative validity of consumer-grade vs. research-grade EEG devices. The most EEG recent research focusing on content validity and attempting to relate a particular ERBP feature or signature to a particular mental state has been generally inconclusive, since the findings are contradictory. In particular, the literature on emotional arousal and valence using IAPS yielded as many studies which find a pattern of reduced Alpha power and increased Theta [66] as those which either find null effects, or even show the opposite pattern, i.e., increased Alpha and decreased Delta and/or Theta [67] as well as increased Beta and Gamma [68; 69; 70]. One possible plausible conclusion that has been repeatedly proposed is that the specific pattern of results related to EEG power spectral distribution may be therefore dependent on design, stimuli, task, context, and individual differences [71]. The findings also vary according to the electrodes considered, whereby, for example, opposite patterns can be observed in frontal/anterior as compared to parietal/posterior sites. An exhaustive review is outside the scope and space of this paper, but it is reasonable to reach similar conclusions by considering recent meta-analyses on the n-Back task and mental workload in general [69]. The major issue in the context of the present study is overlearning and practice effects in that the professional observers serving as participants in our study underwent a lot of pre-experimental repeated exposure with materials, tasks and both devices.