Chen Liangpeng, Liu Yang, Huang Yewei, Li Ziyang, Zhang Chao, Tang Jun, Ling Xueyuan, Cao Bowen, Li Baowang, Zhang Yuan, Zhou Wenjianlong, Xu Qin, Ma Shunchang, Guan Xiudong, Xiao Dan, Geng Jingyao, Zhao Yutong, Li Guolin, Wang Yi-Xuan, Jia Wang, Jiang Yuanwen, Zhang Milin, Li Deling
Department of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China.
Adv Sci (Weinh). 2025 Sep;12(33):e14732. doi: 10.1002/advs.202414732. Epub 2025 May 28.
Accurate decoding of peripheral nerve signals is essential for advancing neuroscience research, developing therapeutics for neurological disorders, and creating reliable human-machine interfaces. However, the poor mechanical compliance of conventional metal electrodes and limited generalization of existing decoding models have significantly hindered progress in understanding peripheral nerve function. This study introduces low-impedance, soft-conducting polymer electrodes capable of forming stable, intimate contacts with peripheral nerve tissues, allowing for continuous and reliable recording of neural activity in awake animals. Using this unique dataset of neurophysiological signals, a neural network model that integrates both handcrafted and deep learning-derived features, while incorporating parameter-sharing and adaptation training strategies, is developed. This approach significantly improves the generalizability of the decoding model across subjects, reducing the reliance on extensive training data. The findings not only deepen the understanding of peripheral nerve function but also open avenues for developing advanced interventions to augment and restore neurological functions.
准确解码外周神经信号对于推进神经科学研究、开发神经系统疾病治疗方法以及创建可靠的人机接口至关重要。然而，传统金属电极较差的机械顺应性以及现有解码模型有限的通用性严重阻碍了对外周神经功能理解的进展。本研究引入了低阻抗、柔软导电聚合物电极，该电极能够与外周神经组织形成稳定、紧密的接触，从而在清醒动物中实现对神经活动的连续可靠记录。利用这个独特的神经生理信号数据集，开发了一种神经网络模型，该模型整合了手工制作和深度学习衍生的特征，同时纳入了参数共享和自适应训练策略。这种方法显著提高了解码模型在不同受试者之间的通用性，减少了对大量训练数据的依赖。这些发现不仅加深了对外周神经功能的理解，还为开发增强和恢复神经功能的先进干预措施开辟了道路。
