Prism-SQA offers interpretable EMG quality assessment, with authors reporting parity or better versus black-box methods
Prism-SQA decomposes surface EMG into clean and five contamination components via U-Net plus bidirectional LSTM, with authors reporting parity or better than black-box methods on Ninapro and dysphagia data.
ImportanceLocalEvidenceE2 unreplicated
You can now inspect the individual impact of five contamination components in surface EMG signals and customise quality criteria without retraining: Prism-SQA uses a U-Net plus bidirectional LSTM to decompose the signal into clean and five contamination components, then checks physiological plausibility via fingerprint verification.
Previous black-box quality assessment methods returned only a single score, showing nothing about which contamination damaged the signal, and changing quality criteria required retraining.
The authors self-report that on Ninapro synthetic-noise data and clinical dysphagia data, performance matches or exceeds black-box methods; the arXiv page notes acceptance to JBHI.
Boundary: results are author self-reported, with no third-party replication yet; the preprint by Kuan-Chen Wang and four others was submitted 11 September and revised 15 September (arXiv:2609.12724).