Human experience in an architectural space is defined as the state of mind that is reflected on their physiological, emotional, and cognitive statuses. Ergonomic data, as an objective manifestation of quantifiable signals generated by the human body during specific spatial perception processes, serves as a vital foundation for spatial evaluation and guidance for optimization. Electroencephalogram (EEG) signals, as quantifiable sensory indicators directly arising from the interaction between individuals and external stimuli, hold substantial potential as a data-driven force and as a means of optimization assessment in the study of generative design. Although existing research has effectively established a unidirectional relationship between EEG and spatial-environment assessment, there is still a notable deficiency in addressing the creation of a two-way, mutually informative feedback mechanism. This study investigates the applicability of EEG signals as a data-driven basis for generative design across universal methods. It delves into various scales and scenarios of digital design, from the microscopic to the macroscopic, encompassing planar and volumetric visual elements, the design of architectural spatial environmental characteristics, and urban space design grounded in human perceptual sightlines. The research examines the viability and appropriateness of an interactive generative design method based on form generation, predicated on human-factor physiological data exemplified by EEG signals. This paper initially conducts a methodological and tool-based examination of current research in ergonomics-driven design and the use of EEG for design assessment, thereby discussing the objective feasibility of employing EEG in interactive generative design. Subsequently, the study establishes an integrated data flow system encompassing multiple hardware and software components to form a comprehensive workflow. Following this setup, empirical studies based on this method are conducted at different scales of application, yielding corresponding form-generative outcomes. Finally, this paper substantiates the rationality and feasibility of this framework in multi environment design domains.
