<![CDATA[Efficiency and optimization in virtual reality (VR) technology is an urgent need, especially in the context of optimizing algorithms to recognize user emotions while using VR. Efficient VR technology can improve user experience and enable more immersive and responsive interactions. This study adopts the preferred reporting items for systematic reviews and meta-analyses (PRISMA) (2020) method to identify and analyze gaps in the existing literature, focusing on the optimization of electroencephalogram (EEG) signal classification algorithms to recognize VR users' emotions. The literature search was conducted through the Scopus database, with article selection based on the type of emotion classified, the classification method used, the limitations of the research, and the results obtained. Of the 1478 articles found, 74 articles passed the initial selection stage, and the final stage 13 articles were selected for further analysis. The selected articles provide important insights into the development of EEG classification algorithms for VR users, especially in multi-user settings. The findings identify potential and opportunities in the development of more efficient and accurate EEG signal classification algorithms for VR users. By focusing on emotion classification in a multi-user VR environment, this research contributes to improving the efficiency of VR technology and supporting a better and more responsive user experience.]]>
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