Data-Driven Recommendation in Brain-Metaverse Interaction
Zihui Qin, Yilin Cao, Jeongyeong Park, Qianru Liu, Zhuohui Xu, Nanlin Jin, Hai‐Ning Liang · 2024
Metaverse offers exciting new ways to experience digital worlds. However, there is limited work to understand and measure such experiences, lacking studies on the real-time interaction between people and metaverse-based virtual worlds, especially using biometrics. This work represents one of the first to investigate this opportunity, focusing on leveraging users' brain signals. We developed a prototype for Brain-Metaverse Interaction (BMI). This paper presents two contributions: (1) real-time interaction enabled by an IoT system. It integrates a virtual reality (VR) headset with brain signals and a multi-communication setup, which enables users to choose the most convenient communication tools from WiFi, Bluetooth, and a cable link; and (2) unlike most existing research and commercial applications to help people relax, our work recommends VR content appropriate to the users' current mental states. This work provides interesting insights into future research of BMI, for example, providing VR content that is adaptive and evolving based on users' mental state and relaxation level.