  Toward user-driven Metaverse applications with fast wireless connectivity and
tremendous computing demand through future 6G infrastructures, we propose a
Brain-Computer Interface (BCI) enabled framework that paves the way for the
creation of intelligent human-like avatars. Our approach takes a first step
toward the Metaverse systems in which the digital avatars are envisioned to be
more intelligent by collecting and analyzing brain signals through cellular
networks. In our proposed system, Metaverse users experience Metaverse
applications while sending their brain signals via uplink wireless channels in
order to create intelligent human-like avatars at the base station. As such,
the digital avatars can not only give useful recommendations for the users but
also enable the system to create user-driven applications. Our proposed
framework involves a mixed decision-making and classification problem in which
the base station has to allocate its computing and radio resources to the users
and classify the brain signals of users in an efficient manner. To this end, we
propose a hybrid training algorithm that utilizes recent advances in deep
reinforcement learning to address the problem. Specifically, our hybrid
training algorithm contains three deep neural networks cooperating with each
other to enable better realization of the mixed decision-making and
classification problem. Simulation results show that our proposed framework can
jointly address resource allocation for the system and classify brain signals
of the users with highly accurate predictions.
