Although a dominant mainstream view is that some aspects of the classification and processing of the signal are deemed to be handled by the integration with machine learning on the basis of massive iterative training applied to large within subject repeated measures, the latter types of approaches are no substitutes for the quality of EEG signal acquisition from the individual subject, that is, they do not directly address the possible problems associated with _weak measurement_ at the individual level. Therefore, the issues partly require approaches and techniques that address small-N or N-of-1 experimental designs, and neither can be _only_ solved with the current mainstream data mining science based on large samples nor with parametrization referenced to a population distribution (see [31]). The issues in EEG personalization largely overlap with the current debate in the multidisciplinary field of measurement which contrasts the **idiographic** person-oriented approach [32; 33] versus the **nomothetic** population distribution referenced approach (for extensive overview and review and discussion within the context of psychological and brain sciences see [34]). Personalized predictions can only be made based on prior data from the individual for whom a prediction is to be made (idiographic data) and not with aggregated data from other individuals (nomothetic data) [35]. This is an implication from the classic ergodicity theorems [36], from which it follows that intra-individual (within subjects) variation (IAV) is equivalent to inter-individual (between subjects) variation (IEV), that is, they are ergodic, only if IAV is homogeneous in time, that is, if there are no fluctuations, trends or other types of time-dependent changes in the IAV time series). If the structure of IAV is heterogeneous, then it can no longer be examined by switching to the IEV perspective--as in most of the approaches relying on population distributions--because the two types of variations are incommensurable. In terms of relevance for neurocognitive processes pertinent to EEG, the non-ergodic implications also apply to functional brain connectivity [37; 38] and the underlying dynamics of neural networks (e.g., see [39]). Thus, employing small-N designs that focus on the individual participant as the _replication unit_ and the _control of him/herself_ is a sound alternative or/and supplementary tool to assess the precision of personalization EEG devices for the measurement of individual mental states. Indeed, there are alternative approaches that derive and conform to the tradition of behavioral [40] and psychophysical [31; 41] experimental designs. One is the numerical method known as Vincentization or Vincentized average [40], in which data from small samples (N = 3-5) are binned so that the derived distribution reflects approximately the same shape as each individual, and a second approach is the item-wise (or by-item) analysis approach [42] in which the dependent variable is aggregated across subjects for a particular item (i.e., stimulus or channel) and consequently the item becomes the unit or case which the statistics is applied to, usually requiring a completely nestedwithin-subjects design. These two methods are complementary and when used together (i.e., item-wise analysis of vincentized data) they offer the opportunity to integrate intra-individual and inter-individual/group variability by empirically constraining or restricting the range of heterogeneity of the combined variance, so that variability can be assumed to be reasonably ergodic. In summary, there seems to be some disparity in the literature regarding the reliability, reproducibility, and accuracy of the Emotiv EPOC headset. Moreover, the literature is specifically lacking in studies investigating other characteristics of the EEG signal such as power and amplitude. The literature is also lacking in studies investigating the precision of EEG features related to a wider variety of mental states such as arousal, valence, and mental load.