Motion Neural Monitoring Based on Flexible Materials: From Signal Acquisition to Training Enhancement

The reliable acquisition of microvolt-level (μV) neural signals (EEG/EMG) during intense physical activity remains a fundamental challenge due to severe motion-induced electromechanical artifacts and dynamic interface instabilities. This review systematically examines how advanced flexible bioelectronics transcend macroscopic mechanical compliance to address the rigorous electrophysiological demands of motion neural monitoring. We elucidate the underlying mechanisms by which engineered materials and electrode structures optimize dynamic interface electrochemistry and equivalent circuit stability. Specifically, we summarize strategies for suppressing half-cell potential fluctuations, preventing parasitic ionic shunt pathways via localized dielectric control (e.g., sweat management), and minimizing piezoresistive noise in dynamic percolation networks. Furthermore, we explore the integration of multimodal systemswhere kinematic sensors serve as dynamic reference vectors for artifact decouplingand their practical applications in neuro-rehabilitation, fatigue management, and motor skill enhancement. Finally, we critically discuss current limitations, emphasizing the necessity for long-term electrochemical robustness, standardized artifact evaluation, and the eventual transition toward AI-driven digital twins. Through this mechanistic analysis, we aim to provide fundamental insights for advancing flexible interfaces from passive wearables to precision motion-neural platforms.