Calibration-Free Electromyography Motor Intent Decoding Using Large-Scale Supervised Pretraining

Alexander Olsson

doi:10.1002/aisy.202500791

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Calibration-Free Electromyography Motor Intent Decoding Using Large-Scale Supervised Pretraining

2026-04-13

DOI：10.1002/aisy.202500791

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Abstract

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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.

Keywords:

electromyography

human–computer interfacing

machine learning

myoelectric control

zero-shot learning