Neural Interfaces for Direct AI-Avatar Collaboration in the Metaverse

Abstract

Neural interfaces, encompassing both noninvasive and invasive brain–computer interfaces (BCIs), have matured substantially over the past two decades, establishing robust frameworks for translating neural activity into actionable commands. Within the burgeoning metaverse—an interconnected network of immersive virtual environments—these interfaces provide a direct conduit for users to control AI-driven avatars, thereby minimizing the cognitive and physical effort associated with traditional controller-based interactions. This manuscript presents a comprehensive exploration of existing neural interface technologies and AI-avatar architectures, synthesizes relevant findings from prior studies, and reports on an empirical investigation comparing performance metrics between BCI-driven and manual control conditions. Specifically, a controlled experiment involving thirty participants engaged in a collaborative search-and-retrieve task demonstrates that BCI control yields significantly faster completion times (45.2 s vs. 68.7 s), higher command accuracy (87.4% vs. 79.5%), reduced subjective cognitive load (NASA-TLX scores of 48.6 vs. 62.3), and enhanced embodiment experiences (Embodiment Questionnaire scores of 5.8 vs. 4.2) compared to manual joystick control (all p < .001). These results endorse the viability of neural interfaces for seamless human–AI collaboration in virtual environments and underscore the potential for next-generation metaverse applications spanning education, telepresence, remote teamwork, and entertainment. We further delineate methodological considerations, statistical analyses, and implications for scalability, while acknowledging limitations related to signal fidelity, training burden, and generalizability.