Cross-Recording Handwritten Digit Decoding from sEMG Using a Compact CNN-Transformer and Few-Shot Adaptation

Authors

Makarova, A. V., Golitsyna, M. V., Lebedev, M. A.

Abstract

Surface electromyography (sEMG) offers a silent and wearable input modality, but its practical use is limited by variability across users and recording sessions. This study presents a compact CNN-Transformer model for decoding isolated handwritten digits from eight-channel sEMG signals. The model combines trainable signal preprocessing, convolutional feature extraction, and Transformer-based temporal modeling. It was evaluated on ten recordings from five participants using recording-seen classification, leave-one-recording-out (LORO) generalization, and few-shot adaptation. The model achieved a mean macro F1 score of 0.924 +- 0.059 in the recording-seen setting and 0.619 +- 0.252 under zero-shot LORO evaluation. Adaptation using two labeled trials per digit increased macro F1 to 0.828 +- 0.112, while ten trials per digit achieved 0.925 +- 0.053. The proposed architecture also outperformed classical and neural baselines in the controlled LORO benchmark. These results indicate that compact CNN-Transformer models, combined with lightweight target-recording calibration, provide a promising basis for adaptive sEMG-based input systems.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 19 Aug 2026.

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