Tracing the theoretical, critical, and empirical dimensions of a specific and persistent problem, this thesis examines why EEG-based neurophysiological research, despite its growing presence in architectural practice, remains concentrated in the design guideline phase and has not found comparable adoption in design development, the phase in which neurophysiological data would be most directly integrated into the act of designing itself.
By tracing the historical and philosophical foundations from which neuroarchitecture emerged, the thesis argues that the field has developed a sufficiently robust theoretical and methodological base to support integration across all design phases. It then examines the role and limits of neuroscience in architectural practice, arguing that its most meaningful contribution lies not in the production of universal standards but in extending the reach of empathic design judgment toward the full diversity of embodied human conditions that architecture is obliged to serve. This knowledge is most meaningfully generated when neuroscience is brought into the act of designing itself, in direct response to the specific inhabitants and context at hand.
To understand the reasons for this disparity, the thesis applies a three-level framework of computational complexity to the forty-two studies identified in the systematic review and bibliometric analysis of Zhao et al. (2025). High-complexity research, requiring machine learning, real-time brain-computer interface systems, and custom algorithm development, increases from twelve percent in the guideline phase to eighty-three percent in the design development phase, while studies accessible without specialist computer science training fall from ninety-one percent to sixteen percent across the same range.
This disparity is not rooted in philosophical or theoretical inadequacy but in the technical and infrastructural demands that design development research places on its practitioners, demands that the field has not yet developed accessible and adequate tools to meet. Whether neuroscience will find its place in the act of designing depends on the degree to which the field turns its attention toward building the accessible tools, pipelines, and shared datasets that make this integration possible.