Whiten, Then Mix: A Two-Part Adapter for Few-Shot sEMG Personalization

Personalizing a gesture decoder to a new sEMG band wearer is conventionally evaluated by the gain from that wearer's own calibration data, but this quantity does not distinguish transferable improvement from wearer-specific benefit. A correction that closes a between-wearer gap may also benefit other wearers. We introduce a cross-wearer substitution diagnostic: each correction is fitted on a different wearer and applied to the recipient. Using this diagnostic, the two axes show different transfer behavior: cross-wearer amplitude corrections transfer at every measured depth, whereas cross-wearer electrode-position corrections widen the gap. Under an invertible linear mixing model with identity source covariance, the population spatial covariance identifies the symmetric polar factor but not the orthogonal one. This distinction motivates our two-part few-shot adapter: Euclidean Alignment performs label-free, wearer-specific whitening, while ElectrodeMix adapts the residual on the electrode axis with $c^{2}g$ learned parameters beyond the classifier head. Meta-pretraining uses a restricted inner loop to support this adaptation. Across the evaluated sEMG archives and both backbones, the method achieves the highest mean accuracy in every 10-shot cross-dataset setting. On EPN612-to-UCI-EMG transfer with ViM-Tiny, it exceeds the frozen probe for all 34 wearers, by 6.9 percentage points on average, using 1,024 adapter parameters versus 6.8M for full fine-tuning. This diagnostic requires only an additional cross-wearer fit and distinguishes transferable improvement from the advantage of recipient-specific fitting.