基于脑电图的空间元件优化设计方法

Zihuan Zhang, Zao Li, Zhe Guo
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引用次数: 0

摘要

在数字设计领域,空间环境设计与人为因素之间的互动研究是近期的一个热门话题。脑电图(EEG)和眼动追踪可作为建筑空间评估的定量分析方法;然而,现有的基于人为因素的空间环境质量改善研究结论往往仍停留在定性分析上。为了实现从人体生理数据出发对空间元素进行定量优化设计,本研究采用了数字空间优化方法和感知评价研究。这样,就建立了一种利用人体心理指标对建筑空间要素进行实时优化的方法。首先,该方法利用人体脑电信号中 "沉思值 "和 "注意值 "的特定指标,以 ThinkGear AM(TGAM)模块为优化目标,以建筑空间颜色和窗户大小为优化对象,以多目标遗传算法为优化工具。其次,本研究结合虚拟现实场景和参数化联动模型,实现了这一优化方法,建立了工具平台和工作流程。第三,本研究以典型居住空间的优化为例,招募了 50 名志愿者参与优化实验。结果表明,在多目标遗传算法的迭代优化下,特定脑电指数明显下降,迭代过程中in-dex的标准偏差波动减小,进一步说明本研究建立的以特定脑电指数为优化目标的优化方法是有效可行的。此外,本研究还为今后更多的脑电图指标和更复杂的空间要素优化研究奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

EEG-based spatial elements optimisation design method

EEG-based spatial elements optimisation design method

EEG-based spatial elements optimisation design method

EEG-based spatial elements optimisation design method

In the field of digital design, a recent hot topic is the study of the interaction between spatial environment design and human factors. Electroencephalogram (EEG) and eye tracking can be used as quantitative analysis methods for architectural space evaluation; however, conclusions from existing studies on improving the quality of spatial environments based on human factors tend to remain qualitative. In order to realise the quantitative optimisation design of spatial elements from human physiological data, this research used the digital space optimisation method and perceptual evaluation research. In this way, it established an optimisation method for built space elements in real-time using human psychological indicators. Firstly, this method used the specific indicators of the Meditation value and Attention value in the human EEG signal, taking the ThinkGear AM (TGAM) module as the optimisation objective, the architectural space colour and the window size as the optimisation object, and the multi-objective genetic algorithm as the optimisation tool. Secondly, this research combined virtual reality scenarios and parametric linkage models to realise this optimisation method to establish a tool platform and workflow. Thirdly, this study took the optimisation of a typical living space as an example and recruited 50 volunteers to participate in an optimisation experiment. The results indicated that with the iterative optimisation of the multi-objective genetic algorithm, the specific EEG index decreases significantly and the standard deviation of the in-dex fluctuates and decreases during the iterative process, which further indicates that the optimisation method established in this study with the specific EEG index as the optimisation objective is effective and feasible. In addition, this study laid the foundation for more EEG indicators and more complex spatial element opti-misation research in the future.

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