Public surface electromyography (EMG) datasets vary widely in electrode layout, channel count, frequency support, and size.
Simply mixing them for pretraining can misalign channel semantics, introduce spectral targets that some devices cannot observe, and let large or high-channel-count datasets dominate learning.
We introduce EMGBlend
It combines shared channel patches with geometry-aware attention, restricts spectral targets to each recording's supported frequency band, and balances exposure across data sources.
Pretraining and Evaluation
We pretrain a 109M-parameter model on 11 public EMG sources and evaluate it on gesture recognition, continuous-force regression, and contact classification.
EMGBlend consistently outperforms matched random initialization and waveform reconstruction controls.
Fixed-budget source controls
Fixed-budget source controls show that multi-source pretraining improves gesture recognition and remains competitive for force decoding.
Ablations
Ablations confirm that geometry, band-aware targets, and source balancing each contribute to transfer, although cross-person NinaPro force estimation remains difficult.
Overall
Overall, EMGBlend shows how heterogeneous EMG datasets can be combined through explicit mechanism design rather than simple concatenation.
Code is available at https://github.com/tamanano/EMGBlend.