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.

翻译：本文提出了一种新颖的框架，利用空中脑机接口（BCI）学习元宇宙用户的期望。通过解读用户的脑部活动，我们的框架有助于优化物理资源并提升用户体验质量（QoE）。为实现这一目标，我们借助无线边缘服务器（WES）通过上行无线信道处理脑电图（EEG）信号，从而消除元宇宙用户设备的计算负担。因此，WES能够学习人类行为、调整系统配置并分配无线电资源，以定制个性化用户设置。尽管BCI潜力巨大，但固有的噪声无线信道和EEG信号的不确定性使得相关的资源分配与学习问题极具挑战性。我们将联合学习与资源分配问题建模为混合整数规划问题。我们的解决方案涉及两种算法：混合学习算法和元学习算法。混合学习算法能够有效求解所构建的问题。具体而言，元学习算法可进一步利用多个用户间EEG信号的神经多样性，从而提高分类准确率。基于真实世界BCI数据集的大量仿真结果表明，我们的框架在低延迟和高EEG信号分类准确率方面具有有效性。