Organize Once, Adapt Efficiently: Target-Conditioned Hypergraph Retrieval for Few-Shot Motor Imagery EEG Adaptation

Rapid deployment of motor imagery–based brain–computer interfaces (BCIs) requires reliable decoding for new users with few calibration trials. Prior source-selection methods reuse historical electroencephalography (EEG) data through subject-level screening, trial-level scoring, or pairwise graph propagation. However, exploiting higher-order relations among trials for budget-constrained, target-conditioned source-trial retrieval remains challenging. To address this, we view transfer utility not as an intrinsic pointwise property but as a target-dependent structural quantity jointly determined by local discriminative coherence and cross-domain reproducibility, and propose a neural retrieval framework over a reusable dual-relation source hypergraph. Following an organize-once, retrieve-per-target paradigm, we build this hypergraph offline with local semantic and cross-domain consensus relations. Online, we query this hypergraph with a small target support set to construct a subhypergraph and select transferable trials through structural propagation and budget-constrained ranking. Cross-subject and cross-dataset experiments on BNCI2014-001, Cho2017, and Lee2019-MI show that, in some settings, our method matches or outperforms the All-source training reference while using as little as 2.35% of the available source trials for target adaptation.