Reliable detection and prediction of neural activity and behavior requires a user model of brain activity that dynamically adapts based on known time-dependent physiological processes, as well as unknown traits of the user. We have applied wireless electroencephalography (EEG) sensors, edge devices with feedback capability, and cloud-assisted data acquisition to realtime and longitudinal brain monitoring and alerting. Toward a user model of brain function, we collected neural and behavioral data from humans in the field. The data replicate previous findings that were obtained under tight laboratory control, suggesting that the methods that we describe will be useful for user modeling of human brain activity under more natural conditions. Specifically, we report that frontal cortex oscillations reorganized with age. Focusing on time-varying aspects of behavior, we then found that performance on memory-intensive cognitive tasks declined during the day. Next, we examined interactions between neural activity and behavioral performance. We report that neural activity and performance co-varied and that this co-variation depended on the cognitive task in ways that were, again, consistent with previous laboratory studies. Lastly, we report the foundations of an adaptive model based on this system that will enable dynamic personalization tailored to each user.