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PRODUCT DESIGN SCHEME DECISION-MAKING METHOD COMBINING ELECTROENCEPHALOGRAM AND EYE MOVEMENT WITH USER SIMILARITY

机译:产品设计方案决策方法与用户相似性结合脑电图和眼睛运动

摘要

A product design scheme decision-making method combining electroencephalogram and eye movement with user similarity. The method comprises three parallel steps: (1) collecting original electroencephalogram (EEG) signals and removing artifacts and noises, extracting a β waveband of EEG about an emotion awakening degree and an α waveband of EEG about an emotional valence, and extracting potential changes of event-related potentials ERPs during decision maker scheme selection; (2) collecting an attention degree obtained on the basis of eye movement behavior data; and (3) obtaining a scheme selection time duration of the decision maker to obtain a scheme preference degree. The three data is combined to obtain implicit decision-making behavior data, and explicit decision-making data is obtained according to the selections of the decision makers; an association degree between the decision-making behavior data of different decision makers is calculated to obtain the user similarity. According to the method, explicit decision-making behaviors and implicit decision-making behaviors are collected, a correlation between the decision makers and schemes is established and a recommendation order is calculated, and an optimal scheme set based on a user similarity relationship is formed.
机译:一种产品设计方案决策方法,将脑电图和眼睛运动与用户相似性结合。该方法包括三个平行步骤:(1)收集原始脑电图(EEG)信号并消除伪像和噪声,提取脑电图的β波段关于情绪觉得脑电图的情绪觉得和α波段,提取潜在的变化与事件相关的潜力ERP在决策者方案选择期间; (2)收集在眼球运动行为数据的基础上获得的注意程度; (3)获得决策者的方案选择时间持续时间,以获得方案偏好程度。组合三个数据以获得隐式决策行为数据,并且根据决策者的选择获得了显式决策数据;计算不同决策者的决策行为数据之间的关联程度以获得用户相似性。根据该方法,收集了明确的决策行为和隐式决策行为,建立了决策者和方案之间的相关性,并且计算了一种推荐顺序,并且形成基于用户相似关系的最佳方案集。

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