Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders,traumatic i...Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders,traumatic injuries,and neurological diseases.Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor,sensory,and cognitive functions,significantly improving patients'quality of life.This review analyzes the chronological development and integration of various neural machine interface technologies,including regenerative peripheral nerve interfaces,targeted muscle and sensory reinnervation,agonist–antagonist myoneural interfaces,and brain–machine interfaces.Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and highresolution electrodes,which enhance the performance and longevity of neural machine interface technology.However,significant challenges remain,such as signal interference,fibrous tissue encapsulation,and the need for precise anatomical localization and reconstruction.The integration of advanced signal processing algorithms,particularly those utilizing artificial intelligence and machine learning,has the potential to improve the accuracy and reliability of neural signal interpretation,which will make neural machine interface technologies more intuitive and effective.These technologies have broad,impactful clinical applications,ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation.This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering,clinical surgery,and neuroengineering to develop more sophisticated and reliable interfaces.By addressing existing limitations and exploring new technological frontiers,neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation,promising enhanced mobility,independence,and quality of life for individuals with neurological impairments.By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles,researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users.展开更多
Brain-machine interface could give patients back their speech,sight or mobility In The Matrix,the protagonist connects to the Matrix to navigate between the real and virtual worlds.In Ghost in the Shell,the brain-mach...Brain-machine interface could give patients back their speech,sight or mobility In The Matrix,the protagonist connects to the Matrix to navigate between the real and virtual worlds.In Ghost in the Shell,the brain-machine interface becomes an important bridge between man and machine.These science fiction works show a technology that was as fascinating as it was inaccessible-until recently.This interface,which for a long time only existed in fantasy,is now appearing in the real world.展开更多
The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatl...The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.展开更多
The post-Moore's era has boosted the progress in carbon nanotube-based transistors.Indeed,the 5 G communication and cloud computing stimulate the research in applications of carbon nanotubes in electronic devices....The post-Moore's era has boosted the progress in carbon nanotube-based transistors.Indeed,the 5 G communication and cloud computing stimulate the research in applications of carbon nanotubes in electronic devices.In this perspective,we deliver the readers with the latest trends in carbon nanotube research,including high-frequency transistors,biomedical sensors and actuators,brain–machine interfaces,and flexible logic devices and energy storages.Future opportunities are given for calling on scientists and engineers into the emerging topics.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supported in part by the National Natural Science Foundation of China,Nos.81927804(to GL),82260456(to LY),U21A20479(to LY)Science and Technology Planning Project of Shenzhen,No.JCYJ20230807140559047(to LY)+3 种基金Key-Area Research and Development Program of Guangdong Province,No.2020B0909020004(to GL)Guangdong Basic and Applied Research Foundation,No.2023A1515011478(to LY)the Science and Technology Program of Guangdong Province,No.2022A0505090007(to GL)Ministry of Science and Technology,Shenzhen,No.QN2022032013L(to LY)。
文摘Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders,traumatic injuries,and neurological diseases.Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor,sensory,and cognitive functions,significantly improving patients'quality of life.This review analyzes the chronological development and integration of various neural machine interface technologies,including regenerative peripheral nerve interfaces,targeted muscle and sensory reinnervation,agonist–antagonist myoneural interfaces,and brain–machine interfaces.Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and highresolution electrodes,which enhance the performance and longevity of neural machine interface technology.However,significant challenges remain,such as signal interference,fibrous tissue encapsulation,and the need for precise anatomical localization and reconstruction.The integration of advanced signal processing algorithms,particularly those utilizing artificial intelligence and machine learning,has the potential to improve the accuracy and reliability of neural signal interpretation,which will make neural machine interface technologies more intuitive and effective.These technologies have broad,impactful clinical applications,ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation.This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering,clinical surgery,and neuroengineering to develop more sophisticated and reliable interfaces.By addressing existing limitations and exploring new technological frontiers,neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation,promising enhanced mobility,independence,and quality of life for individuals with neurological impairments.By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles,researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users.
文摘Brain-machine interface could give patients back their speech,sight or mobility In The Matrix,the protagonist connects to the Matrix to navigate between the real and virtual worlds.In Ghost in the Shell,the brain-machine interface becomes an important bridge between man and machine.These science fiction works show a technology that was as fascinating as it was inaccessible-until recently.This interface,which for a long time only existed in fantasy,is now appearing in the real world.
基金supported by the Japanese Government,Grants-in-Aid for Scientific Research 2014 to 2016 under Grant No.26330296
文摘The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.
基金the financial funds of the National Key Research and Development Program of China(2016YFA02019042017YFB0405400)+12 种基金the Project of“20 items of University”of Jinan(2018GXRC031)NSFC(No.52022037)Taishan Scholars Project Special Funds(tsqn201812083)the NSFC(51802116)supported by NSFC(52002165)the Natural Science Foundation of Shandong Province,China(Grant No.ZR2019BEM040)Beijing National Laboratory for Molecular Science(BNLMS202013)Guangdong Provincial Natural Science Foundation(2021A1515010229)Shenzhen Basic Research Project(JCYJ20210317150714001)the Innovation Project for Guangdong Provincial Department of Education(2019KTSCX155)the National Science Foundation China(NSFC,Project 52071225)the National Science Center and the Czech Republic under the ERDF program“Institute of Environmental Technology—Excellent Research”(No.CZ.02.1.01/0.0/0.0/16_019/0000853)the Sino-German Research Institute for support(Project No.GZ 1400)。
文摘The post-Moore's era has boosted the progress in carbon nanotube-based transistors.Indeed,the 5 G communication and cloud computing stimulate the research in applications of carbon nanotubes in electronic devices.In this perspective,we deliver the readers with the latest trends in carbon nanotube research,including high-frequency transistors,biomedical sensors and actuators,brain–machine interfaces,and flexible logic devices and energy storages.Future opportunities are given for calling on scientists and engineers into the emerging topics.
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.
