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Addressing the challenge of subject variability and network constraints in motor-imagery brain-computer interfaces, this paper evaluates NEXUS-MI, a gateway-coordinated federated personalization framework that treats edge synchronization as a coupled communication-and-learning control problem. By retaining classifier heads locally while dynamically managing shared backbone updates across multi-session EEG benchmarks (BCICIV-2a and OpenBMI), the authors benchmark adaptive synchronization policies against ideal-link baselines under heterogeneous network conditions. The policy reduces server-to-client communication overhead by approximately 42% with negligible aggregate accuracy loss, yet exposes critical subject-level vulnerabilities where individual classification accuracy drops by up to 12 percentage points.
Federated learning can cut edge BCI bandwidth by 42% without harming cohort-level accuracy, but aggregate benchmarks mask severe individual personalization failures of up to 12 percentage points under realistic network lag.
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.
