Closed-Loop Deep Brain Stimulation Platform for Translational Research - Li, Yan; Nie, Yingnan; Li, Xiao; Cheng, Xi; Zhu, Guanyu; Zhang, Jianguo; Quan, Zhaoyu; Wang, Shouyan (Neuromodulation (Malden, Mass.), 2025)
This study aims to facilitate the translation of innovative closed-loop deep brain stimulation (DBS) strategies from theory to practice by establishing a research platform. The platform addresses the challenges of real-time stimulation artifact removal, low-latency feedback stimulation, and rapid translation from animal to clinical experiments. The platform comprises hardware for neural sensing and stimulation, a closed-loop software framework for real-time data streaming and computation, and an algorithm library for implementing closed-loop DBS strategies. The platform integrates hardware for both animal and clinical research. The closed-loop software framework handles the entire closed-loop stimulation, including data streaming, stimulation artifact removal, preprocessing, a closed-loop stimulation strategy, and stimulation control. It provides a unified programming interface for both C/C++ and Python, enabling secondary development to integrate new closed-loop stimulation strategies. Additionally, the platform includes an algorithm library with signal processing and machine learning methods to facilitate the development of new closed-loop DBS strategies. The platform can achieve low-latency feedback stimulation control with response times of 6.23 ± 0.85 ms and 6.95 ± 1.11 ms for animal and clinical experiments, respectively. It effectively removed stimulation artifacts and demonstrated flexibility in implementing new closed-loop DBS algorithms. The platform has integrated several typical closed-loop protocols, including threshold-adaptive DBS, amplitude-modulation DBS, dual-threshold DBS and neural state–dependent DBS. This work provides a research tool for rapidly deploying innovative closed-loop strategies for translational research in both animal and clinical studies. The platform’s capabilities in real-time data processing and low-latency control represent a significant advancement in translational DBS research, with potential implications for the development of more effective therapeutic interventions.