embedded-sEMG-signal-quality-dataset

This repository contains the anonymized surface electromyography (sEMG) dataset and the reproducible Jupyter Notebook processing workflow associated with the study “IEC 60601-2-40-Based Evaluation of an Embedded sEMG Platform for Muscle Activation Analysis.” The dataset includes multichannel sEMG recordings acquired from 10 healthy participants during controlled isometric and isotonic biceps contraction protocols. Signals were acquired using an embedded acquisition platform based on an ADS1298 analog front-end and an STM32 microcontroller at a sampling frequency of 2 kHz. The repository includes the raw anonymized CSV recordings, a documented Python/Jupyter Notebook, processing parameters, generated tables, and figure-generation routines. The workflow performs dataset inspection, automatic subject and condition detection, segmentation into 20 s windows, signal conditioning, signal-quality metric calculation, paired statistical analysis, NeuroKit2-based envelope extraction, Savitzky–Golay smoothing, inter-channel Spearman correlation analysis, and reproducible figure/table generation. This repository is intended to support transparency, reproducibility, and independent verification of the signal-quality assessment and inter-channel activation analysis reported in the manuscript. The workflow does not rename or modify the original CSV files; file-name normalization is performed only internally for metadata extraction.