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Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

sEMG is vulnerable to various contaminants that distort signal morphology and spectral content. Accurate signal quality assessment (SQA) is essential for identifying such degradation and ensuring reliable clinical analyses and decisions. Recent neural network-based SQA methods achieve accurate quality estimation by learning complex contamination patterns, yet their black-box nature prevents clinicians from understanding or validating the reported scores and limits adaptability to application-specific quality definitions without retraining. To address these limitations, we propose Prism-SQA, an

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