发表机构
Beijing University of Posts and Telecommunications; Shenzhen University; Dexwise（北京邮电大学; 深圳大学; Dexwise）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对表面肌电数据异构性，提出自监督框架EMGBlend，通过共享通道补丁、几何注意力、频带限制和源平衡，在11个数据源上预训练109M模型，提升手势识别与力解码性能。
AI 中文摘要
公开的表面肌电（EMG）数据集在电极布局、通道数量、频率支持和规模上差异很大。简单地将它们混合用于预训练可能会导致通道语义错位，引入某些设备无法观测的频谱目标，并让大型或高通道数的数据集主导学习过程。我们提出了EMGBlend，一个针对这些差异设计的自监督框架。它结合了共享通道补丁与几何感知注意力，将频谱目标限制在每个记录支持频带内，并平衡各数据源的暴露程度。我们在11个公开EMG数据源上预训练了一个109M参数的模型，并在手势识别、连续力回归和接触分类任务上进行了评估。EMGBlend持续优于匹配的随机初始化和波形重建对照组。固定预算的数据源控制实验表明，多源预训练提高了手势识别性能，并在力解码方面保持竞争力。消融实验证实，几何、频带感知目标和数据源平衡各自对迁移有贡献，尽管跨受试者的NinaPro力估计仍然困难。总体而言，EMGBlend展示了如何通过显式机制设计而非简单拼接来组合异构EMG数据集。代码可在以下网址获取：此https URL
英文摘要
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, a self-supervised framework designed around these differences. 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. 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 show that multi-source pretraining improves gesture recognition and remains competitive for force decoding. Ablations confirm that geometry, band-aware targets, and source balancing each contribute to transfer, although cross-person NinaPro force estimation remains difficult. 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
