# Review of Studies on User Research Based on EEG and Eye Tracking
## 1 Introduction  
Using verbal feedback for user research is plagued by a degree of vagueness, and the results can easily be influenced by various factors. Although modern technology has yielded many efficient research methods and data analysis tools, achieving an objective assessment of user experience is still a challenge, and is one of the current hotspots of research [1]. With the rise of neuroscience and its research methods, electroencephalogram (EEG) research is increasingly being used to understand users' true thoughts. Combining eye tracking (ET) and EEG research is a current and future trend in the field of user research. With the help of joint research between the two, we can obtain more accurate, in-depth, and comprehensive user information compared to subjective reports, making it possible to objectively understand what users see and think. In studies related to neuroscience, the characteristic of EEG data is high temporal resolution (milliseconds) compared to other brain imaging techniques such as fMRI [2]. This feature is often used to classify techniques, and is crucial for user research because it allows for the identification of neurophysiological correlations exposed to stimuli within a functional time window. Moreover, standard EEG equipment is non-invasive, and allows participants to operate normally in the laboratory or at the test site. In [PERSON]'s [3] research, computer and portable EEG systems can easily conduct ERP research. [PERSON] et al. [4] studied human-computer interaction in gaming activities based on low-cost EEG signaltechnology, greatly expanding the possible use of ERP methods in various new environments. Furthermore, the cost of electroencephalography is much lower than other brain imaging techniques, and is one of the most readable and promising neuroscience tools in user research [5]. Eye trackers have become popular as user research tools for recording user gaze paths. Eye-tracking research records the movement trajectory of the eyes during specific tasks, in order to analyze the process of user cognitive behavior and psychological activity. Through eye-tracking data, we can analyze users' gaze patterns to evaluate and optimize product designs [6]. [PERSON] et al. [7] quantified the visual aesthetic quality of product design by applying eye-tracking technology. [PERSON] et al. [8] collected gaze data on yachts using eye-tracking devices, and then evaluated the attractiveness of design parameters using eye-tracking tools such as areas of interest (AOI), scan paths, and heat maps. [PERSON] et al. [9] used eye-tracking technology to explore the relationship between product cognition and design decisions. Eye-tracking research is based on users' actual operations, and can objectively analyze products that are used in daily life to derive reasonable rules or conclusions. However, the various indicators of eye-tracking data themselves reflect behavioral results and cannot directly reflect cognitive and thinking processes. Behavior is the comprehensive result of various brain activities, so the referential significance of the data itself is not singular, and is difficult to explain. Eye-tracking research requires good experimental design to correctly interpret eye-tracking data, or it needs to be combined with interviews or retrospective tests to reflect cognitive and thinking processes. [PERSON] et al. 's [10] research showed that the combination of EEG and eye-tracking can be successfully used to study natural and salient attention shifts. [PERSON]'s [11] research showed that using EEG activity alone as a predictive indicator for self-regulation cannot correctly determine emotional responses to affective stimuli. However, combining different data sources with eye-tracking synchronization can overcome the limitations of single detection methods, providing added value for many different research fields. [PERSON] et al.