Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment
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 interpretable and adaptable neural framework that reformulates SQA as a physiology-aware source-...
| Authors | Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh et al. |
|---|---|
| Primary category | eess.SP |
| Published | 11 September 2026 |
| Last updated at source | 15 September 2026 |
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