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In this paper, we provide guidance for the organization and implementation of EEG studies. This work was inspired by our experience conducting a large-scale, multi-site study, but many elements could be applied to any EEG project. Section 1 focuses on study activities that take place before data collection begins. Topics covered include: establishing and training study teams, considerations for task design and piloting, setting up equipment and software, development of formal protocol documents, and planning communication strategy with all study team members. Section 2 focuses on what to do once data collection has already begun. Topics covered include: (1) how to effectively monitor and maintain EEG data quality, (2) how to ensure consistent implementation of experimental protocols, and (3) how to develop rigorous preprocessing procedures that are feasible for use in a large-scale study. Links to resources are also provided, including sample protocols, sample equipment and software tracking forms, sample code, and tutorial videos (to access resources, please visit: https://osf.io/wdrj3/).

ERP experiments generate massive datasets, often containing thousands of values for each participant, even after averaging. The richness of these datasets can be very useful in testing sophisticated hypotheses, but this richness also creates many opportunities to obtain effects that are statistically significant but do not reflect true differences among groups or conditions (bogus effects). The purpose of this paper is to demonstrate how common and seemingly innocuous methods for quantifying and analyzing ERP effects can lead to very high rates of significant but bogus effects, with the likelihood of obtaining at least one such bogus effect exceeding 50% in many experiments. We focus on two specific problems: using the grand-averaged data to select the time windows and electrode sites for quantifying component amplitudes and latencies, and using one or more multifactor statistical analyses. Reanalyses of prior data and simulations of typical experimental designs are used to show how these problems can greatly increase the likelihood of significant but bogus results. Several strategies are described for avoiding these problems and for increasing the likelihood that significant effects actually reflect true differences among groups or conditions.

In this paper, I explain why I adopted a Research Domain Criteria (RDoC) approach to study the neurobiology of auditory verbal hallucinations (AVH), or voices. I explain that the RDoC construct of "agency" fits well with AVH phenomenology. To the extent that voices sound nonself, voice hearers lack a sense of agency over the voices. Using a vocalization paradigm like those used with nonhuman primates to study mechanisms subserving the sense of agency, we find that the auditory N1 ERP is suppressed during vocalization, that EEG synchrony preceding speech onset is related to N1 suppression, and that both are reduced in patients with schizophrenia. Reduced cortical suppression is also seen across multiple psychotic disorders and in clinically high-risk youth, but it is not related to AVH. The motor activity preceding talking and connectivity between frontal and temporal lobes during talking have both proved sensitive to AVH, suggesting neural activity and connectivity associated with intentions to act may be a better way to study agency and predictions based on agency.