Institute of Medical Science and Technology, Shahid Beheshti University (SBU), Tehran, Iran.
School of Architecture and Environmental Design, University of Science and Technology (IUST), Tehran, Iran.
HERD. 2023 Apr;16(2):284-309. doi: 10.1177/19375867221133135. Epub 2023 Jan 20.
This systematic review aims to strengthen the relationship between architecture and neuroscience by classifying data measurement techniques in the field of neuroarchitecture with a focus on the most practical and common methodological approaches. It classifies data recording techniques in different architectural categories (e.g., interior, urban, built environment).
With regard to urban life developments and technological breakthroughs, studies of human interactions with environments have been expanding in recent years. Additionally, recent advances in neuroscience have allowed architects to find out more about human experiences in built environments, but there are few valid frameworks about what methodologies and instruments are more common to conduct experimental tasks in this interdisciplinary field.
Twenty-eight experimental studies were selected based on the preferred reporting items for systematic reviews and meta-analyses literature search extension (PRISMA) systematic review protocol and a comprehensive analysis. The task-space of selected articles was categorized into three subfields, namely, "interior design," "urban design," and "building design" based on environments and their stimuli. As for this context-based categorization, recording techniques and methodology were distinguished for each subfield division.
More than 50% of the studies were incorporated in the first two categories, and the EEG recording was the most frequently employed neuroimaging technique thanks to the technical efficacy of its setup and the high temporal resolution of its electrophysiological signals.
In this study, a summary of techniques and methodological approaches applied in the field is provided in a nut shell, and a general framework of instruments is presented to help scholars to carry out more practical research in the future leading to designing built environments more efficiently.
本系统综述旨在通过对神经建筑学领域的数据测量技术进行分类，重点关注最实用和常见的方法学方法，从而加强建筑学与神经科学之间的联系。它将数据记录技术分为不同的建筑类别（例如，室内、城市、建筑环境）。
随着城市生活的发展和技术的突破，近年来，人们对人类与环境相互作用的研究一直在不断扩大。此外，神经科学的最新进展使建筑师能够更多地了解人类在建筑环境中的体验，但在这个跨学科领域中，进行实验任务更常用的方法学和仪器方面，几乎没有有效的框架。
根据系统评价和荟萃分析文献检索扩展（PRISMA）系统综述方案和全面分析，选择了 28 项实验研究。根据环境及其刺激，将选定文章的任务空间分为“室内设计”、“城市设计”和“建筑设计”三个子领域。对于这种基于上下文的分类，为每个子领域划分区分了记录技术和方法。
超过 50%的研究被纳入前两个类别，由于其设置的技术功效及其电生理信号的高时间分辨率，脑电图记录是最常使用的神经影像学技术。
在这项研究中，概括了该领域应用的技术和方法学方法，并提出了一个通用的仪器框架，以帮助学者在未来更有效地进行更实际的研究，从而更有效地设计建筑环境。
