# Review of Studies on User Research Based on EEG and Eye Tracking
## 5 Discussion
### Future Research Prospects
#### 5.3.2 Universal Research on Multi-Technology Fusion  
In recent years, user research has drawn a lot of experience from more mature fields such as the cognitive neuroscience of memory, language, and emotion. With clear experimental goals, matched research objects, practical experimental design and research methods, strong fault tolerance, and improvements in the level of technology, deeper content research has been conducted based on obtaining effective conclusions, gradually exploring the psychological characteristics of user aesthetics, cognition, and experience processes [109].  
On the one hand, from a technological perspective, with the continuous progress of AR, VR, and artificial intelligence technologies, high-intensity blurred reality may be able to compensate for the lack of accuracy of physiological signal measurement devices to a certain extent [110], and can make physiological signal recognition and classification more accurate. For example, [PERSON] et al. [111] proposed an emotion recognition method for EEG signals based on the integrated learning method AdaBoost, and the recognition accuracy was significantly improved. Therefore, a multidisciplinary fusion approach is essential. The introduction of statistics and computer technology will help establish more comprehensive mathematical models. The quality of quantitative models for user emotions, cognition, and experience will be much better than previous evaluation models, and the refinement level will be improved. AI (artificial intelligence), ML (machine learning), and DL (deep learning) technologies have great potential for development in the fields of driving, aviation, education, and healthcare, in predicting cognitive workload. By analyzing EEG signals and brain imaging data such as MRI, the frequency and amplitude of brainwaves can be analyzed to evaluate the structure and function of the brain for predicting and assessing cognitive workload [112]. In addition, these technologies can participate in behavior and task analysis, real-time monitoring, and feedback, using machine learning algorithms to analyze user behavior data to identify patterns and features of high cognitive workload under specific tasks, and provide personalized feedback based on the monitoring results. Moreover, training machine learning models with large amounts of EEG and brain imaging data can predict and classify cognitive workload, interpret AI models [113], implement real-time health monitoring systems, and quantitatively evaluate EEG biomarkers [114], thereby improving work efficiency and safety.  
On the other hand, research has verified that the measurement results of the fusion of EEG signals and eye tracking signals are generally better than those for single signals [115]. Currently, domestic and foreign scholars are trying to collect user emotions by integrating multiple physiological quantities, such as EEG signals and eye tracking signals, skin electrical signals, breathing, and heart rate, in order to explore measurement methods that are compatible with different user-differentiated needs in ubiquitous methods. In the long run, accurate measurement is a prerequisite for quantifying emotions as reference elements to guide product and service design. The construction of this multi-channel input feedback mechanism will help predict the images and preferences of different types of users efficiently and accurately, and improve methods of multi-technology fusion in the field of user research. This is an effective way to guide design practice, provide supportive objective data, reduce decision subjectivity and one-sidedness, and promote the universality of similar and related research.