Objective: The purpose of this study is to analyze research cases that utilize EEG in the field of User Experience (UX) and User Interface (UI) through a systematic literature review.
Background: UX and UI design are key elements in the development of products and services. As EEG-based analysis of biometric signals gains traction, it provides valuable insights into user behavior and cognitive states. This research aims to explore the application of EEG in UX/UI design, identify its limitations, and propose solutions to enhance its effectiveness.
Method: In this study, a systematic review of relevant studies published between 2014 and 2024 inclusive was conducted. Various UX/UI evaluation methodologies and the applicability of EEG data were analyzed using the PICO framework. The review identifies existing research gaps and proposes future directions for EEG-based UX/UI design research.
Results: The studies focused on user experience, emotion, and cognitive load in contexts such as VR, vehicle interfaces, and product design. Various techniques, including EEG, eye tracking, and facial expression recognition, were employed together. Complex interfaces and low usability caused high cognitive load and negative emotions, while intuitive and highly usable designs improved immersion and information recall. In EEG analysis, alpha, beta, theta, and gamma waves were used as the main signals, and emotional and cognitive states were quantitatively evaluated. Consequently, EEG has demonstrated its potential as an effective tool for UX/UI design and evaluation.
Conclusion: The findings indicate that simplified and intuitive design elements can enhance user engagement and reduce cognitive load. EEG-based analysis has proven to be a powerful tool for quantitative usability testing of the impact of design factors on user experience. However, challenges such as limited demographic diversity, EEG data analysis complexity, and controlled laboratory settings remain. Addressing these challenges requires expanding research to diverse user groups, adopting wireless EEG devices, and advancing real-time data analysis capabilities.
Application: This study provides insights into the potential of EEG in UX/UI design and serves as a reference for future research in EEG-based UX/UI design and evaluation.