From Classical Filtering to Adaptive and AI-Driven Paradigms: A PRISMA-Based Systematic Review and Bibliometric Analysis of EMG Signal Filtering

Abstract

Electromyography (EMG) is a widely used technique for assessing muscle activity in response to electrical stimulation of the nervous system. Nevertheless, EMG signals are inherently weak, non-stationary, and highly susceptible to noise contamination, necessitating the use of advanced acquisition and signal processing strategies. This study presents a comprehensive systematic review and bibliometric analysis of EMG signal filtering and data acquisition frameworks. Unlike previous reviews that focus solely on signal processing techniques, this study uniquely integrates Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based systematic screening with multi-dimensional bibliometric mapping to reveal methodological convergence, research gaps, and future design paradigms for low-cost and embedded EMG systems. Bibliometric analyses were performed using Biblioshiny and VOSviewer to characterize the intellectual structure of the field. The primary objective is to identify recent advances, dominant research trends, and critical knowledge gaps warranting further investigation. Based on PRISMA screening, 173 peer-reviewed articles were included for analysis. The findings reveal a sustained annual growth rate of 3.71% in EMG filtering research and highlight five key analytical dimensions: (1) performance evaluation, (2) science mapping, (3) research collaboration networks, (4) citation dynamics, and (5) emerging trends and future research opportunities.