Neural Digital Twins: Toward Next-Generation Brain-Computer Interfaces

2026-01-04

arXiv.org

AI Summary

Current brain–computer interfaces (BCIs) struggle to achieve long-term, high-precision control due to challenges such as frequent recalibration necessitated by neural plasticity, cross-session instability, real-time processing latency, and limited generalization capability. This work proposes a novel framework—Neural Digital Twin (NDT)—by introducing the digital twin paradigm into BCI for the first time, establishing a personalized neural computational model capable of real-time evolution. The NDT framework integrates multimodal neural data, adaptive machine learning, and real-time signal processing to dynamically predict brain states and optimize decoding strategies. Experimental results demonstrate that NDT significantly enhances system accuracy, robustness, and individual adaptability, offering a new paradigm for next-generation, highly usable neural decoding systems.

Technology Category

Cognitive Modeling & Cognitive Systems: Neural Spike CodingHumans and AI: Brain-Sensing and AnalysisMachine Learning: Bio-inspired Learning

Application Category

User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendationResponsible Web: Machine-in-the-loop, human agency and autonomyEconomics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• brain-computer interfaces
• neuroplasticity
• real-time processing
• personalization
• neural decoding

Innovation

• Neural Digital Twin
• brain-computer interface
• adaptive decoding
• personalized neurotechnology
• real-time neural modeling