Current neural interfaces such as brain-computer interfaces (BCIs) face several fundamental challenges, including frequent recalibration due to neuroplasticity and session-to-session variability, real-time processing latency, limited personalization and generalization across subjects, hardware constraints, surgical risks in invasive systems, and cognitive burden in patients with neurological impairments. These limitations significantly affect the accuracy, stability, and long-term usability of BCIs. This article introduces the concept of the Neural Digital Twin (NDT) as an advanced solution to overcome these barriers. NDT represents a dynamic, personalized computational model of the brain-BCI system that is continuously updated with real-time neural data, enabling prediction of brain states, optimization of control commands, and adaptive tuning of decoding algorithms. The design of NDT draws inspiration from the application of Digital Twin technology in advanced industries such as aerospace and autonomous vehicles, and leverages recent advances in artificial intelligence and neuroscience data acquisition technologies. In this work, we discuss the structure and implementation of NDT and explore its potential applications in next-generation BCIs and neural decoding, highlighting its ability to enhance precision, robustness, and individualized control in neurotechnology.

翻译：当前神经接口（如脑机接口，BCI）面临若干根本性挑战，包括因神经可塑性和会话间变异导致的频繁重新校准、实时处理延迟、跨被试的有限个性化与泛化能力、硬件限制、侵入式系统的外科手术风险，以及神经功能缺损患者的认知负荷。这些限制显著影响了BCI的准确性、稳定性和长期可用性。本文提出神经数字孪生（NDT）的概念，作为克服这些障碍的先进解决方案。NDT代表一种动态的、个性化的脑-BCI系统计算模型，能够通过实时神经数据持续更新，从而实现脑状态预测、控制指令优化以及解码算法的自适应调谐。NDT的设计灵感来源于数字孪生技术在航空航天和自动驾驶等先进工业领域的应用，并融合了人工智能与神经科学数据采集技术的最新进展。本工作讨论了NDT的架构与实现方法，并探讨其在下一代BCI与神经解码中的潜在应用，重点阐述了其在提升神经技术精度、鲁棒性和个体化控制能力方面的优势。