Abstract

In this study, we propose an electroencephalogram (EEG)-based long short-term memory networks model for recognizing user preferences toward architectural design images. An EEG is an approach that records the electrical activity in the brain, and EEG-based affection recognition is a technique used for quantitatively recognizing human emotion by analysing the recorded signals. Decision-makers’ subjective reactions toward architectural design alternatives may play a key role in the architectural planning and design stage. In this regard, the proposed model enables the quantitative recognition of their preferences and supports architects in the planning and design stages. The suggested model classifies the recorded data using a deep-learning technique. To build the model, an EEG recording experiment was conducted with 18 subjects, who were asked to select their most/least preferred images among eight images of small-housing design. Post recording, a positive and negative affect schedule questionnaire was distributed to the subjects to rate their affection. Google TensorFlow and Keras were used to structure the model. After training, precision, recall, and f1 score metrics were used to evaluate and validate the model. This model can help designers to evaluate design alternatives in terms of decision-making. Moreover, as this model uses biosignal data, which is universal to humans, architectural design processes for children, the elderly, etc., may be supported. Furthermore, a data-driven design database may be proposed in a future research for cross-validating with previous methods such as interviews and observations.

中文翻译：

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使用脑电图和长短期记忆网络来识别用户对建筑设计替代方案的设计偏好☆

摘要

在这项研究中，我们提出了一种基于脑电图（EEG）的长期短期记忆网络模型，用于识别用户对建筑设计图像的偏好。EEG是一种记录大脑中电活动的方法，基于EEG的情感识别是一种用于通过分析记录的信号来定量识别人类情感的技术。决策者对建筑设计替代方案的主观反应可能在建筑规划和设计阶段发挥关键作用。在这方面，提出的模型能够定量地识别他们的偏好，并在规划和设计阶段为建筑师提供支持。建议的模型使用深度学习技术对记录的数据进行分类。为了建立模型，我们对18名受试者进行了脑电图记录实验，他们被要求从八幅小型住房设计图像中选择最/最不喜欢的图像。记录后，向受试者分发正面和负面影响时间表调查表以评估他们的感情。使用Google TensorFlow和Keras构建模型。训练后，精度，召回率和f1得分指标用于评估和验证模型。该模型可以帮助设计人员评估决策方案。此外，由于该模型使用了人类通用的生物信号数据，因此可以支持儿童，老人等的建筑设计过程。此外，在未来的研究中可能会提出一个数据驱动的设计数据库，用于与以前的方法（如访谈和观察）进行交叉验证。