EMG Analysis Engine (Module A): An Open-Source, IEEE/ISEK-Compliant Platform for Reproducible EMG Signal Processing and Feature Extraction

Electromyography (EMG) remains one of the most informative biosignals in clinical and research settings, yet its widespread adoption is stifled by a fragmented ecosystem of expensive proprietary systems and undocumented research pipelines that resist reproducibility. We present the EMG Analysis Engine (Module A), an open-source, modular signal processing platform engineered to IEEE/ISEK standards, designed to close this translational gap. The system implements a zero-phase 4th-order Butterworth bandpass filter (20-450 Hz), a 50 Hz notch filter, and automatic signal-to-noise ratio quality gating, followed by gold-standard feature extraction-Mean Absolute Value (MAV), Root Mean Square (RMS), Zero-Crossing Rate (ZCR), Waveform Length (WL), and Slope Sign Changes (SSC)-through a Streamlit-based interface accessible to non-specialist users. Core signal processing logic is fully decoupled from the interface layer, guaranteeing computational reproducibility and enabling independent component substitution. Outputs follow a standardized JSON schema designed for downstream integration with gait analysis and surgical robotics pipelines. Preliminary validation on synthetic signals with known ground truth demonstrates pipeline stability and feature fidelity; external validation on the publicly available Ninapro benchmark dataset (Atzori et al., 2014) is ongoing and will be reported in a subsequent extension. The architecture was developed with traceability and configuration management as first-class concerns, aligning with principles that underpin medical device software standards such as ISO 13485 and IEC 62304-a deliberate foundation for future regulatory-pathway development. By democratizing access to reliable, transparent EMG feature extraction, this platform lowers barriers for students, clinicians, and researchers alike, establishing a reusable foundation for an expanding ecosystem targeting intelligent prosthetics and surgical robotics.