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

arXiv:2609.25582 · cs.LG, cs.AI · Submitted 2026-09-22 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/tamanano/EMGBlend

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 11× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Linear probe — 3× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Nadia — 1× in this paper · explained in 2 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• TinyMyo: a Tiny Foundation Model for Flexible EMG Signal Processing at the Edge
• SPECTRE: Spectral Pre-training Embeddings with Cylindrical Temporal Rotary Position Encoding for Fine-Grained sEMG-Based Movement Decoding
• NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models
• Neural Codecs as Biosignal Tokenizers

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