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

arXiv: https://arxiv.org/abs/2609.12724 · HTML: https://mlsanity.com/paper/2609.12724/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 56 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Music Source Separation in the Waveform Domain](https://mlsanity.com/paper/1911.13254/) (2019)
- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation](https://mlsanity.com/paper/1805.02410/) (2018)
- [On loss functions and evaluation metrics for music source separation](https://mlsanity.com/paper/2202.07968/) (2022)
- [The Three Ghosts of Medical AI: Can the Black-Box Present Deliver?](https://mlsanity.com/paper/2012.06000/) (2020)
- [Audio Source Separation Using Variational Autoencoders and Weak Class Supervision](https://mlsanity.com/paper/1810.13104/) (2018)
- [ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks](https://mlsanity.com/paper/2210.13271/) (2022)
- [TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement](https://mlsanity.com/paper/2410.03843/) (2024)
- [Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet](https://mlsanity.com/paper/2402.17701/) (2024)
- [A Fast, robust algorithm for power line interference cancellation in neural recording](https://mlsanity.com/paper/1402.6862/) (2014)
- [A joint separation-classification model for sound event detection of weakly labelled data](https://mlsanity.com/paper/1711.03037/) (2017)
- [Towards Robust and Accurate Myoelectric Controller Design based on Multi-objective Optimization using Evolutionary Computation](https://mlsanity.com/paper/2204.02179/) (2022)
- [A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals](https://mlsanity.com/paper/2402.05482/) (2024)

## Abstract (arXiv)

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-separation and verification process. Prism-SQA decomposes each input signal into a clean sEMG component and five contaminant-specific components using a U-Net with bidirectional long short-term memory. Each separated contaminant component is examined by a Contaminant Fingerprint Verifier, which enforces physiological plausibility by comparing its temporal and spectral structure with canonical contaminant signatures. This design allows clinicians to inspect how each contaminant affects signal quality, grounding the assessment in transparent, signal-level evidence rather than opaque latent representations. Quality indices computed from the verified components further enable customization of quality criteria across clinical contexts without retraining. We evaluate Prism-SQA on continuous quality-score estimation using synthesized noisy sEMG from public Ninapro datasets and on binary quality classification using a clinical dysphagia dataset. Results show that Prism-SQA achieves competitive or better performance than contemporary black-box neural methods while providing explicit interpretability and adaptability, advancing toward practical and clinically aligned sEMG SQA.