In model-based engineering, model merging requires a high cognitive load on developers, as it requires identifying, reconciling, and integrating changes across parallel artifacts. Recent studies of EEG microstates have demonstrated their potential to reveal neural dynamics during complex mental tasks. However, studies on how these microstates specifically manifest during model‐merge operations remain scarce. To address this gap, in this work we apply, for the first time, EEG microstate analysis to characterize with high temporal resolution the neural dynamics during software-merge tasks presented in two representational formats—sentential (Java source code) and diagrammatic (UML class diagrams). To this end, we conducted a controlled experiment in which 35 developers completed merge problems while we continuously recorded their EEG. In alternating cycles of eyes-closed rest and merge-task execution, we extracted both resting and task segments for microstate analysis. Our findings revealed a diametrically opposite pattern along the two cognitive axes: as participants transitioned from rest to task, microstate parameters changed in opposite directions depending on representation. In the sentential, microstates A, C and D exhibited highly significant increases in duration, occurrence and coverage, while B showed only a slight prolongation in duration. Conversely, in the diagrammatic axis, states A and C underwent pronounced reductions, B declined moderately, and D maintained stable duration but decreased in occurrence and coverage. This study reinforces the feasibility of using microstate metrics as fine‐grained biomarkers of cognitive overload in model‐integration operations, opening the way for the development of cognitively aware tools and more efficient, safer integration practices in software engineering, laying the groundwork for adaptive development environments that could automatically adjust abstraction levels or issue high‐load alerts.