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.
ImportanceLocalPreuvesE2 non réplicable
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).