Study of Mental Model in Human-Computer Interaction Based on EEG Signal Data

Mental model refers to the internal cognitive structure and reasoning mechanism in the human brain, and its influence on users’ psychological preferences and behavior decisions mainly occurring at the subconscious level, which is difficult to comprehensively and accurately evaluated by subjective self-reports in the conscious state. Taking human-computer interaction tasks and scenarios in e-commerce shopping as the background, this study makes an exploratory study on the classification and characteristics of users’ mental models based on EEG signal data. It was found that the parameters of α wave, β wave and cognitive load in EEG signal can better reflect the mental model differences of users in the aforementioned interaction process. On this basis, the clustering method and optimal classification of typical mental models are analyzed, and the comparative analysis shows that the K-means method can yield optimal results for refining the typical mental models of the users. The study provides a new basis and reference for the design of human-computer interaction system.