TY - GEN
T1 - Data-Driven Recommendation in Brain-Metaverse Interaction
AU - Qin, Zihui
AU - Cao, Yilin
AU - Park, Jeongyeong
AU - Liu, Qianru
AU - Xu, Zhuohui
AU - Jin, Nanlin
AU - Liang, Hai Ning
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Brain signal
KW - Data mining
KW - Internet of things
KW - Metaverse
UR - http://www.scopus.com/inward/record.url?scp=85207498991&partnerID=8YFLogxK
U2 - 10.1109/ICSCC62041.2024.10690613
DO - 10.1109/ICSCC62041.2024.10690613
M3 - Conference Proceeding
AN - SCOPUS:85207498991
T3 - 2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
SP - 343
EP - 348
BT - 2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Smart Computing and Communication, ICSCC 2024
Y2 - 25 July 2024 through 27 July 2024
ER -