Design decision making is happened in every design node and iteration, and the expert decision-making bias and personal preference will ultimately affect the success or failure of the product reaching the market. In this paper, we try to predict the design decision making by investigating the relations between design decision making and subjects’ eye movements and Electroencephalogram(EEG) response. Four different methods were applied and compared to classify the different EEG features and two methods were used for EEG feature selection to correspond the design decision making results. In this study, the authors applied a multimodal fusion strategy for design decision making recognition where the authors used eye tracking and EEG response data as input dataset. According to the experiment results, the performance of the fusion strategy combined with EEG signals and eye movement characteristics is well in fitting the expert decision making results. The multimodal fusion combining eye tracking data and EEG has a strong potential to be a new design decision method to guide the design practice and provide supportive and objective data to reduce the effects of subjectivity, one-sidedness and superficiality in decision making. These results show that it is possible to create a classifier based on features extracted from eye movements and EEG response for the design decision making behaviour.

设计决策发生在每个设计节点和迭代中，专家决策的偏见和个人偏爱最终将影响产品投放市场的成败。在本文中，我们试图通过研究设计决策与受试者的眼动和脑电图（EEG）反应之间的关系来预测设计决策。应用了四种不同的方法并进行了比较，以对不同的脑电特征进行分类，并使用两种方法进行脑电特征选择，以对应设计决策结果。在这项研究中，作者将多模式融合策略应用于设计决策识别，其中作者使用眼动追踪和EEG响应数据作为输入数据集。根据实验结果，融合策略结合脑电信号和眼动特性的性能非常适合专家的决策结果。结合了眼动数据和脑电图的多峰融合技术具有很强的潜力，可以成为指导设计实践并提供支持性和客观性数据的新设计决策方法，以减少决策过程中主观性，单面性和表面性的影响。这些结果表明，可以根据眼动和脑电图响应提取的特征来创建分类器，以进行设计决策。结合了眼动数据和脑电图的多峰融合技术具有很强的潜力，可以成为指导设计实践并提供支持性和客观性数据的新设计决策方法，以减少决策过程中主观性，单面性和表面性的影响。这些结果表明，可以根据眼动和脑电图响应提取的特征来创建分类器，以进行设计决策。结合了眼动追踪数据和脑电图的多峰融合技术有可能成为指导设计实践并提供支持性和客观性数据的新设计决策方法，以减少决策过程中主观性，单面性和表面性的影响。这些结果表明，可以根据眼动和脑电图响应提取的特征来创建分类器，以进行设计决策。