基于脑电的用户感知意象思维表征

发布时间：2018-01-28 08:17

  本文关键词： 工业设计 产品意象 事件相关电位 产品造型感知　出处：《机械设计》2017年06期 　论文类型：期刊论文

【摘要】：为更客观地获取用户的产品意象数据,应用事件相关电位技术探讨了用户感知意象的思维表征。对154个国际知名品牌中的2 500款汽车造型进行筛选,得到经典车型720款。以16名大学生用户作为被试者,分别从中选择美观、中等和不美观图片各60幅,应用事件相关电位技术测试这些用户在欣赏各类图片时的脑电信号,并应用口语分析法获取其心理活动。结果显示,在第100 ms和第200 ms时,在大脑额叶Fz附近产生了脑电成分N1和P2;在第440 ms和第650~920 ms时,在大脑顶叶Cz,Cp1,Cp2和Pz位置产生了中期成分和晚成分。结果表明:漂亮、独特、协调、流畅、硬朗和简洁是大学生用户感知汽车造型的主要判断标准。在该感知过程中,用户在Cz,Cp1,Cp2及Pz位置的中期成分和晚成分可表征大脑思维,且两种成分的波幅可作为评价产品意象值的重要指标。该研究结果有助于提升产品意象获取手段的客观性,并有望应用于产品造型设计。
[Abstract]:In order to obtain the user's product image data more objectively, the thinking representation of the user's perceived image was discussed by using event-related potential technology. The 2,500 models of the 154 international famous brands were screened. We got 720 classic models and took 16 college students as the subjects. We chose 60 beautiful, medium and unattractive pictures from each of them. The electroencephalogram (EEG) signals of these users were measured by event-related potentials and their psychological activities were obtained by oral analysis. The results showed that at 100ms and 200ms respectively. The brain electrical components N1 and P2were produced near FZ in the frontal lobe of the brain. At the 440ms and 650s 920ms, the metaphase and late components were produced in the Cp1Cp2 and Pz position of the apical lobe Cz. the results showed that they were beautiful, unique and coordinated. Fluency, hardness and simplicity are the main criteria for college students to perceive car modeling. In the process of perception, the user is in CzCp1. The intermediate and late components of Cp2 and PZ positions can be used to characterize brain thinking. The amplitude of the two components can be used as an important index to evaluate the value of product image. The results are helpful to enhance the objectivity of product image acquisition and are expected to be used in product modeling design.
【作者单位】： 华南农业大学艺术学院;广东省服装创新设计工程技术研究中心;
【基金】：广东省科技计划资助项目(2014B090904076) 广东省哲学社会科学规划资助项目(GD16CYS10)
【分类号】：TB47
【正文快照】： 产品意象研究通过应用神经网络[1]、遗传算法[2]、主成分分析[3]、Mars[4]等方法构建产品意象与用户感知之间的内在机制,有效提升了产品设计质量。这些研究主要采用语义差异法等方法获取用户感知意象,以解决其模糊性和不确定性[5]。近年来,事件相关电位(Event-Related Potenti，

本文编号：1470239