A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey
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Abstract (arXiv)
The growing interest in the Metaverse has generated momentum for members of academia and industry to innovate toward realizing the Metaverse world. The Metaverse is a unique, continuous, and shared virtual world where humans embody a digital form within an online platform. Through a digital avatar, Metaverse users should have a perceptual presence within the environment and can interact and control the virtual world around them. Thus, a human-centric design is a crucial element of the Metaverse. The human users are not only the central entity but also the source of multi-sensory data that can be used to enrich the Metaverse ecosystem. In this survey, we study the potential applications of Brain-Computer Interface (BCI) technologies that can enhance the experience of Metaverse users. By directly communicating with the human brain, the most complex organ in the human body, BCI technologies hold the potential for the most intuitive human-machine system operating at the speed of thought. BCI technologies can enable various innovative applications for the Metaverse through this neural pathway, such as user cognitive state monitoring, digital avatar control, virtual interactions, and imagined speech communications. This survey first outlines the fundamental background of the Metaverse and BCI technologies. We then discuss the current challenges of the Metaverse that can potentially be addressed by BCI, such as motion sickness when users experience virtual environments or the negative emotional states of users in immersive virtual applications. After that, we propose and discuss a new research direction called Human Digital Twin, in which digital twins can create an intelligent and interactable avatar from the user's brain signals. We also present the challenges and potential solutions in synchronizing and communicating between virtual and physical entities in the Metaverse.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Sep 2023.
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Citing papers per year: 2024: 1; 2025: 5; 2026: 1.
7 papers.
- 2024Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Surveycited by 11
- 2025Transformer-based EEG Decoding: A Surveycited by 3
- 2025Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MRcited by 2
- 2025End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systemscited by 0
- 2025Human Digital Twins in Personalized Healthcare: An Overview and Future Perspectivescited by 0
- 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0
- 2026Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communicationcited by 0
- 2026Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communication24 Mar 2026
- 2025Transformer-based EEG Decoding: A Survey3 Jul 2025
- 2025Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR6 Jun 2025
- 2025Integration of TinyML and LargeML: A Survey of 6G and Beyond20 May 2025
- 2025Human Digital Twins in Personalized Healthcare: An Overview and Future Perspectives15 Mar 2025
- 2025End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems6 Mar 2025
- 2024Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey22 Jan 2024