Built environments play an essential role in our day-to-day lives since people spend more than 85% of their times indoors. Previous studies at the conjunction of neuroscience and architecture confirmed the impact of architectural design features on varying human experience, which propelled researchers to study the improvement of human experience in built environments using quantitative methods such as biometric sensing. However, a notable gap in the knowledge persists as researchers are faced with sensors that are commonly used in the neuroscience domain, resulting in a disconnect regarding the selection of effective sensors that can be used to measure human experience in designed spaces. This issue is magnified when considering the variety of sensor signal features that have been proposed and used in previous studies. This study builds on data captured during a series of user studies conducted to measure subjects’ physiological responses in designed spaces using the combination of virtual environments and biometric sensing. This study focuses on the data analysis of the collected sensor data to identify effective sensors and their signal features in classifying human experience. To that end, we used a feature attribution model (i.e., SHAP), which calculates the importance of each signal feature in terms of Shapley values. Results show that electroencephalography (EEG) sensors are more effective as compared to galvanic skin response (GSR) and photoplethysmogram (PPG) (i.e., achieving the highest SHAP values among the three at 3.55 as compared to 0.34 for GSR and 0.21 for PPG) when capturing human experience in alternate designed spaces. For EEG, signal features calculated from the back channels (occipital and parietal areas) were found to possess comparable effectiveness as the frontal channel (i.e., have similar mean SHAP values per channel). In addition, frontal and occipital asymmetry were found to be effective in identifying human experience in designed spaces.

建筑环境在我们的日常生活中发挥着至关重要的作用，因为人们 85% 以上的时间都在室内度过。先前结合神经科学和建筑的研究证实了建筑设计特征对不同人类体验的影响，这促使研究人员使用生物识别传感等定量方法研究建筑环境中人类体验的改善。然而，由于研究人员面临神经科学领域常用的传感器，因此在知识方面仍然存在显着差距，导致在选择可用于测量设计空间中人类体验的有效传感器方面存在脱节。考虑到之前研究中提出和使用的各种传感器信号特征，这个问题被放大了。这项研究建立在一系列用户研究期间捕获的数据之上，这些研究旨在使用虚拟环境和生物识别传感的组合来测量设计空间中受试者的生理反应。本研究侧重于对收集到的传感器数据进行数据分析，以识别有效的传感器及其信号特征，从而对人类体验进行分类。为此，我们使用了一个特征归因模型（即 SHAP），它根据 Shapley 值计算每个信号特征的重要性。结果表明，与皮肤电反应 (GSR) 和光体积描记图 (PPG) 相比，脑电图 (EEG) 传感器更有效（即在三者中达到最高的 SHAP 值 3.55，而 GSR 为 0.34，PPG 为 0.21）在交替设计的空间中捕捉人类体验。对于脑电图，发现从后通道（枕骨和顶叶区域）计算出的信号特征具有与前通道相当的有效性（即，每个通道具有相似的平均 SHAP 值）。此外，发现额叶和枕骨不对称可有效识别设计空间中的人类体验。