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EMGBlend: Heterogeneity-Aware Self-Supervised Pretraining for Gesture and Force Decoding
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 dom
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