发表机构
University of Notre Dame（圣母大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
NEXUS-MI提出网关协调的通信感知联邦个性化框架，通过将同步视为学习与通信控制问题，在BCICIV-2a和OpenBMI上减少约42%骨干流量，同时保持准确性并揭示受试者水平脆弱性。
AI 中文摘要
基于脑电图（EEG）的运动想象脑-机接口（MI-BCI）在不同受试者和会话之间存在差异，这使得从有限的校准数据中进行个性化变得复杂。联邦学习可以利用共享表示而无需集中原始EEG数据，但现有的联邦MI研究大多假设定期同步。我们提出了NEXUS-MI，一个网关协调的联邦个性化框架，将同步视为一个耦合的学习与通信控制问题。原始EEG和分类器头部保持本地，而边缘协调器维护共享骨干网络。我们通过使用BCI竞赛IV数据集2a（BCICIV-2a；9名受试者，4类）和OpenBMI（54名受试者，2类）进行离线重放来评估NEXUS-MI。会话1支持骨干学习，会话2提供有限校准的个性化和留出测试。一个理想链路参考和六种异构链路策略表征了网关参与、缓冲、过期更新接纳和骨干下载控制。主要比较在固定延迟更新处理的同时，对比非自适应和通信感知同步。配对受试者水平比较使用Holm校正，并通过分层自助法评估五个匹配实现上的稳健性。通信感知协调在两个数据集上将服务器到客户端的骨干流量减少了约42%，而队列水平准确性差异较小且依赖于实现。队列平均值也掩盖了受试者水平的脆弱性，相对于理想链路参考，在BCICIV-2a上损失达到约12个百分点。这些发现确立了网关同步作为联邦MI个性化中的显式设计变量，并激励对个性化准确性、通信成本、更新新鲜度和受试者水平可靠性的联合评估。
英文摘要
Electroencephalography (EEG)-based motor-imagery brain-computer interfaces (MI-BCIs) vary across subjects and sessions, complicating personalization from limited calibration data. Federated learning can exploit shared representations without centralizing raw EEG, but existing federated MI studies largely assume regular synchronization. We introduce NEXUS-MI, a gateway-coordinated federated personalization framework that treats synchronization as a coupled learning-and-communication control problem. Raw EEG and classifier heads remain local, while an edge coordinator maintains the shared backbone. We evaluate NEXUS-MI through offline replay using BCI Competition IV Dataset 2a (BCICIV-2a; 9 subjects, 4 classes) and OpenBMI (54 subjects, 2 classes). Session 1 supports backbone learning, and Session 2 provides limited-calibration personalization and held-out testing. An ideal-link reference and six heterogeneous-link policies characterize gateway participation, buffering, stale-update admission, and backbone-download control. The principal comparison holds delayed-update handling fixed while contrasting non-adaptive and communication-aware synchronization. Paired subject-level comparisons use Holm adjustment, and robustness across five matched realizations is assessed by hierarchical bootstrap. Communication-aware coordination reduced server-to-client backbone traffic by approximately 42% on both datasets, while cohort-level accuracy differences were small and realization-dependent. Cohort averages also concealed subject-level vulnerability, with losses reaching approximately 12 percentage points on BCICIV-2a relative to the ideal-link reference. These findings establish gateway synchronization as an explicit design variable in federated MI personalization and motivate joint evaluation of personalized accuracy, communication cost, update freshness, and subject-level reliability.
