Prediction of product design decision Making: An investigation of eye movements and EEG features |
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| Affiliation: | 1. Department of Psychology, Tsinghua University, Beijing 10084, China;2. Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi''an 710072, China;1. Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, China;2. Delta-NTU Corporate Laboratory for Cyber-Physical System, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;3. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore;1. School of Engineering, University of Glasgow, University Ave., Glasgow, UK;2. Department of Product Design Engineering, Glasgow School of Art, 167 Renfrew St., Glasgow, UK;3. School of Psychology, University of Glasgow, 58 Hillhead St., Glasgow, UK;1. Health Economics Research Unit, University of Aberdeen, Institute of Applied Health Sciences, Foresterhill, Aberdeen, AB25 2QN, United Kingdom;2. School of Psychology, University of Lincoln, United Kingdom |
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| Abstract: | 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. |
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| Keywords: | Eye movements EEG Design decision making Multimodal fusion |
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