SPECTRE: Advancing sEMG-Based Movement Decoding
Published:Dec 27, 2025 05:55
•1 min read
•ArXiv
Analysis
This paper introduces SPECTRE, a novel self-supervised learning framework for decoding fine-grained movements from sEMG signals. The key contributions are a spectral pre-training task and a Cylindrical Rotary Position Embedding (CyRoPE). SPECTRE addresses the challenges of signal non-stationarity and low signal-to-noise ratios in sEMG data, leading to improved performance in movement decoding, especially for prosthetic control. The paper's significance lies in its domain-specific approach, incorporating physiological knowledge and modeling the sensor topology to enhance the accuracy and robustness of sEMG-based movement decoding.
Key Takeaways
- •SPECTRE is a domain-specific self-supervised learning framework for sEMG-based movement decoding.
- •It uses spectral pre-training and a novel Cylindrical Rotary Position Embedding (CyRoPE).
- •SPECTRE outperforms existing methods, including supervised and generic SSL approaches.
- •The framework is designed to address challenges like signal non-stationarity and low SNR in sEMG data.
Reference
“SPECTRE establishes a new state-of-the-art for movement decoding, significantly outperforming both supervised baselines and generic SSL approaches.”