The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep l...The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding.Parallel progress in electrode design—particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies—has enhanced wearability and operational stability.Nevertheless,key challenges persist,including individual variability,biocompatibility limitations,and susceptibility to interference in complex environments.Further validation and optimization are needed to address gaps in generalization capability,long-term reliability,and real-world operational robustness.This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade,highlighting key design principles,material innovations,and integration strategies that are poised to advance non-invasive BCI capabilities.It also discusses the importance of multimodal data fusion,hardware-software co-optimization,and closed-loop control strategies.Furthermore,the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation,aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.展开更多
Neuroprostheses aim to repair and replace damaged sensory brain functions such as vision,hearing and touch,improve cognitive functions such as memory,and control arms through electrical stimulations in motor cortex or...Neuroprostheses aim to repair and replace damaged sensory brain functions such as vision,hearing and touch,improve cognitive functions such as memory,and control arms through electrical stimulations in motor cortex or peripheral nerves.Through review of the progress and status of different neuroprostheses,we found an increasing role of machine learning in achieving complex prosthetic functions with groundbreaking results.This article provides a perspective on the role of machine learning in neuroprostheses designs and envisions future involvement of machine learning for more capable neuroprostheses in revolutionizing the treatment of neurological disorders and disabilities.展开更多
基金the National Natural Science Foundation of China for Distinguished Young Scholars(62325403)the National Natural Science Foundation of China(62504103 and 82002454)+4 种基金the Basic Research Program of Jiangsu(BK20251214)the Natural Science Foundation of Jiangsu Province(BK20230498)the China Postdoctoral Science Foundation under Grant Number 2025T180143 and 2025M770547the Medical Scientific Research Project of Jiangsu Health Commission(ZD2021011)the Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB427)。
文摘The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding.Parallel progress in electrode design—particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies—has enhanced wearability and operational stability.Nevertheless,key challenges persist,including individual variability,biocompatibility limitations,and susceptibility to interference in complex environments.Further validation and optimization are needed to address gaps in generalization capability,long-term reliability,and real-world operational robustness.This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade,highlighting key design principles,material innovations,and integration strategies that are poised to advance non-invasive BCI capabilities.It also discusses the importance of multimodal data fusion,hardware-software co-optimization,and closed-loop control strategies.Furthermore,the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation,aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.
基金supported in part by Science and Technology Innovation(STI)2030 Major Projects,China(No.2021ZD0200400).
文摘Neuroprostheses aim to repair and replace damaged sensory brain functions such as vision,hearing and touch,improve cognitive functions such as memory,and control arms through electrical stimulations in motor cortex or peripheral nerves.Through review of the progress and status of different neuroprostheses,we found an increasing role of machine learning in achieving complex prosthetic functions with groundbreaking results.This article provides a perspective on the role of machine learning in neuroprostheses designs and envisions future involvement of machine learning for more capable neuroprostheses in revolutionizing the treatment of neurological disorders and disabilities.
