arXiv Machine Learning September 14, 2026 By Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh, Sheng-Yu Peng, Yu Tsao

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

Read the original on arXiv Machine Learning →

Prism‑SQA is a neural framework that interprets surface electromyography (sEMG) quality assessment by separating each signal into a clean component and five contaminant-specific components using a U‑Net with bidirectional LSTM. Each contaminant is verified against canonical physiological signatures, allowing clinicians to see how specific noises affect quality. The resulting quality indices can be customized for different clinical contexts without retraining, and Prism‑SQA matches or surpasses current black‑box methods on both continuous and binary quality tasks.

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arXiv Machine Learning

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arXiv AI

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EEG-Xplain: Decoding Neural Black-Boxes of EEG Foundation Models

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