Urban neuromodulation and neurofeedback systems have become increasingly adaptive and closed-loop. However, they still focus primarily on brain signals and often ignore the urban areas where these signals happen. The framework introduced in this study integrates EEG, physiological measures, indicators of environmental exposures, and GIS spatial analysis. Our goal is to incorporate Local Climate Zone classification into one unified cycle of user-specific interpretation. Contrary to the typical methodology in digital exposomes and environmental neuroscience, where the individual-to-environment relationship is assessed for exposure or well-being purposes, our approach treats urban exposure directly as an input to neurofeedback adaptation. In this respect, the proposal involves using EEG together with biological signals, along with spatial and environmental exposure metrics. According to the principal hypothesis, the use of spatial and environmental exposure data may help to lower ambiguity in the interpretation of the neurophysiological signal. The article is organized around four interconnected layers. They are environmental exposure, human sensing, AI-based fusion, and adaptive feedback. The layers describe the model through a non-clinical use case and examine major ethical, technical, and social challenges. The suggested architecture is conceptual and non-clinical. The aim is to orient future empirical and translational studies rather than to report already validated outcomes. The main contribution of the framework is the use of urban exposure as an active contextual input for interpreting brain–body states and guiding adaptive feedback, instead of treating the environment as background noise.