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

TL;DR

This paper proposes Neural Digital Twins (NDT), dynamic personalized models of brain-BCI systems, aiming to improve accuracy, stability, and adaptability of next-generation brain-computer interfaces through real-time data integration.

Contribution

It introduces the concept of NDT, inspired by Digital Twin technology, as a novel approach to address key challenges in current BCIs by enabling adaptive, personalized brain modeling.

Findings

NDT can predict brain states in real-time.

NDT enhances BCI robustness and personalization.

Potential to reduce calibration and improve long-term stability.

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.