@article{52891a08-4e38-4077-9b2c-39c1ac3ff718,
  abstract     = {{<p>Machine learning algorithms for myoelectric pattern recognition require substantial user-specific training data, limiting broader applications of electromyography (EMG) in human–computer interfacing. Here, we present a framework for EMG-mediated motor intent decoding designed to function for new users without collecting user-specific training data. We introduce a Transformer-based architecture, termed the Spatially Aware Feature-learning Transformer (SAFT), which processes EMG time windows with variable numbers of channels from arbitrary spatial electrode configurations by combining channel-wise temporal feature extraction with learned spatial encoding of electrode positions and attention across channels. This enables training of a single model across heterogeneous EMG datasets. In the present study, large-scale supervised pretraining refers to pretraining on a pooled corpus of 29 public EMG databases comprising 506 subjects, 108 movement classes, and ≈9.9 million nonrest EMG windows after preprocessing. A pretrained SAFT model was fine-tuned on a held-out database and evaluated for cross-user performance. On the 3DC benchmark, the pretrained-only model achieved 28.7% balanced accuracy (vs. 10% chance), while pretrained and fine-tuned cross-user SAFT models achieved 81.8% balanced accuracy, comparable to conventional user-specific linear discriminant analysis (LDA) models (82.9%). These findings indicate the feasibility of EMG intent decoding models that work “out of the box” without end-user calibration.</p>}},
  author       = {{Olsson, Alexander E. and Maleševic, Nebojša and Björkman, Anders and Antfolk, Christian}},
  issn         = {{2640-4567}},
  keywords     = {{electromyography; human–computer interfacing; machine learning; myoelectric control; zero-shot learning}},
  language     = {{eng}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Advanced Intelligent Systems}},
  title        = {{Calibration-Free Electromyography Motor Intent Decoding Using Large-Scale Supervised Pretraining}},
  url          = {{http://dx.doi.org/10.1002/aisy.202500791}},
  doi          = {{10.1002/aisy.202500791}},
  year         = {{2026}},
}