  This article proposes a novel framework that utilizes an over-the-air
Brain-Computer Interface (BCI) to learn Metaverse users' expectations. By
interpreting users' brain activities, our framework can help to optimize
physical resources and enhance Quality-of-Experience (QoE) for users. To
achieve this, we leverage a Wireless Edge Server (WES) to process
electroencephalography (EEG) signals via uplink wireless channels, thus
eliminating the computational burden for Metaverse users' devices. As a result,
the WES can learn human behaviors, adapt system configurations, and allocate
radio resources to tailor personalized user settings. Despite the potential of
BCI, the inherent noisy wireless channels and uncertainty of the EEG signals
make the related resource allocation and learning problems especially
challenging. We formulate the joint learning and resource allocation problem as
mixed integer programming problem. Our solution involves two algorithms that
are a hybrid learning algorithm and a meta-learning algorithm. The hybrid
learning algorithm can effectively find the solution for the formulated
problem. Specifically, the meta-learning algorithm can further exploit the
neurodiversity of the EEG signals across multiple users, leading to higher
classification accuracy. Extensive simulation results with real-world BCI
datasets show the effectiveness of our framework with low latency and high EEG
signal classification accuracy.
