EEG-based spatial elements optimisation design method.

Area of Science:

• Digital Design
• Human-Computer Interaction
• Architectural Psychology

Background:

• Evaluating architectural spaces often relies on qualitative human factor assessments.
• Existing methods lack quantitative approaches for optimizing spatial elements based on human physiological responses.
• Electroencephalogram (EEG) and eye tracking offer potential for objective spatial evaluation.

Purpose of the Study:

• To establish a quantitative optimization method for architectural space elements using human psychological indicators from EEG.
• To develop a real-time optimization system for built space elements.
• To bridge the gap between qualitative human factors research and quantitative design optimization.

Main Methods:

• Utilized Meditation and Attention values from human EEG signals (ThinkGear AM module).
• Employed a multi-objective genetic algorithm as the optimization tool.
• Integrated virtual reality scenarios and parametric linkage models to create a tool platform and workflow.
• Conducted an optimization experiment on a typical living space with 50 volunteers.

Main Results:

• Demonstrated significant decreases in specific EEG indicators during iterative optimization.
• Observed fluctuations and decreases in the standard deviation of EEG indices throughout the optimization process.
• Validated the effectiveness and feasibility of the proposed EEG-based optimization method.

Conclusions:

• The developed method provides a quantitative approach for optimizing architectural spaces based on human physiological data.
• This research lays the groundwork for future studies incorporating more EEG indicators and complex spatial element optimizations.
• The findings support the integration of human psychological responses into digital design processes for enhanced environments.