BrainNEST: Negotiated Share with Routed Memory in Federated Continual Learning of Large EEG Models

Large EEG Models (LEMs) learn transferable representations from diverse datasets, yet their knowledge must keep pace with new tasks, recording protocols, and subject populations. These datasets arrive over time and remain distributed across institutions, calling for a model that can learn collaboratively while retaining earlier capabilities. We present the first systematic study of federated continual adaptation of pretrained LEMs across evolving EEG datasets and task families. Our framework, BrainNEST, organizes adaptation around evidence-gated knowledge consolidation. Clients encode multiscale channel and frequency-band structure into compact sketches. The server establishes partial cross-client correspondences and uses this evidence to control both the blockwise contribution of local updates and their allocation to shared or routed state. Routed parameter memory retains updates for subsequent reuse, retrieving offsets by structural and acquisition compatibility to initialize future clients. Experiments on 14 datasets spanning seven BCI task families and two pretrained backbones demonstrate improved adaptation, retention, and transfer to unseen datasets. BrainNEST provides a practical approach to maintaining LEMs as distributed EEG resources continue to grow.