Adaptive Closed-Loop Control of Sensory Nerve Stimulation in Neuromonitoring Systems A Predictive and Case-Specific Neuroengineering Approach

This monograph presents a novel approach to adaptive, closed-loop sensory neuromonitoring, introducing predictive and case-specific stimulation control comparable to advanced motor pathway monitoring systems. Conventional intraoperative sensory monitoring relies on passive observation of somatosensory evoked potentials (SSEPs), limiting real-time adaptability and early detection of neural compromise. The proposed system integrates multi-electrode stimulation, real-time signal acquisition, adaptive control algorithms, and machine learning–based predictive models to actively modulate sensory nerve activation. Experimental validation demonstrates significant improvements in signal stability, amplitude preservation, latency accuracy, predictive detection, and energy efficiency compared with conventional methods. The framework enables patient-specific calibration, early warning of neural stress, and automated stimulation adjustments, ensuring safer and more precise intraoperative monitoring. This work establishes a new paradigm in neuromonitoring, transforming sensory nerve monitoring from reactive measurement into intelligent, predictive neurocontrol, and provides a foundation for future developments in surgical neuroprotection and neuroengineering applications.       
Keywords  
Sensory Neuromonitoring, Closed-loop Control, Neural Engineering, Intraoperative Monitoring, Sensory Nerve Stimulation, Predictive Neuroprotection, Biomedical Signal Processing, Adaptive Neurocontrol