Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices,such as robot arms,computer curs...Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices,such as robot arms,computer cursors,and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper,two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced,the PNN decoder and the modified PNN (MPNN) decoder. In the ex-periment,rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity,and pressure was recorded by a pressure sensor synchronously. After training,the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their per-formances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder,with a CC of 0.8657 and an MSE of 0.2563,outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance,indicating that the MPNN decoder can handle different tasks in BMI system,including the detection of movement states and estimation of continuous kinematic parameters.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
This review describes work presented in the 2014 inaugural Tsinghua University Press-Springer Nano Research Award lecture, as well as current and future opportunities for nanoscience research at the interface with bra...This review describes work presented in the 2014 inaugural Tsinghua University Press-Springer Nano Research Award lecture, as well as current and future opportunities for nanoscience research at the interface with brain science. First, we briefly summarize some of the considerations and the research journey that has led to our focus on bottom-up nanoscale science and technology. Second, we recapitulate the motivation for and our seminal contributions to nanowire- based nanoscience and technology, including the rational design and synthesis of increasingly complex nanowire structures, and the corresponding broad range of ＂applications＂ enabled by the capability to control structure, com- position and size from the atomic level upwards. Third, we describe in more detail nanowire-based electronic devices as revolutionary tools for brain science, including （i） motivation for nanoelectronics in brain science, （ii） demonstration of nanowire nanoelectronic arrays for high-spatial/high-temporal resolution extracellular recording, （iii） the development of fundamentally-new intracellular nanoelectronic devices that approach the sizes of single ion channels, （iv） the introduction and demonstration of a new paradigm for innervating cell networks with addressable nanoelectronic arrays in three-dimensions. Last, we conclude with a brief discussion of the exciting and potentially transformative advances expected to come from work at the nanoelectronics-brain interface.展开更多
This paper presents the first report of a system of human's speech interaction with rats via integration of brain–machine interfaces and automatic speech recognition technologies. We propose a novel human–rat sp...This paper presents the first report of a system of human's speech interaction with rats via integration of brain–machine interfaces and automatic speech recognition technologies. We propose a novel human–rat speech interaction paradigm by incorporating speech translator module, which translates human's speech commands into suitable electrical brain stimulation to steer the rat to induce expected locomotor behaviors. The preliminary results show that we can guide a rat's movement by speech commands. We further look into the future application scenarios together with forthcoming challenges facing this newly evolved cyborg intelligent system. This work will pave the way for natural interaction with animal robots.展开更多
Brain machine interfaces (BMIs) have demonstrated lots of successful arm-related reach decoding in past decades, which provide a new hope for restoring the lost motor functions for the disabled. On the other hand, the...Brain machine interfaces (BMIs) have demonstrated lots of successful arm-related reach decoding in past decades, which provide a new hope for restoring the lost motor functions for the disabled. On the other hand, the more sophisticated hand grasp movement, which is more fundamental and crucial for daily life, was less referred. Current state of arts has specified some grasp related brain areas and offline decoding results; however, online decoding grasp movement and real-time neuroprosthetic control have not been systematically investigated. In this study, we obtained neural data from the dorsal premotor cortex (PMd) when monkey reaching and grasping one of four differently shaped objects following visual cues. The four grasp gesture types with an additional resting state were classified asynchronously using a fuzzy k-nearest neighbor model, and an artificial hand was controlled online using a shared control strategy. The results showed that most of the neurons in PMd are tuned by reach and grasp movement, us- ing which we get a high average offline decoding accuracy of 97.1%. In the online demonstration, the instantaneous status of monkey grasping could be extracted successfully to control the artificial hand, with an event-wise accuracy of 85.1%. Overall, our results inspect the neural firing along the time course of grasp and for the first time enables asynchronous neural control of a prosthetic hand, which underline a feasible hand neural prosthesis in BMIs.展开更多
基金Project supported by the National Natural Science Foundation of China (Nos. 30800287 and 60703038)the Natural Science Foundation of Zhejiang Province, China (No. Y2090707)
文摘Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices,such as robot arms,computer cursors,and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper,two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced,the PNN decoder and the modified PNN (MPNN) decoder. In the ex-periment,rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity,and pressure was recorded by a pressure sensor synchronously. After training,the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their per-formances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder,with a CC of 0.8657 and an MSE of 0.2563,outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance,indicating that the MPNN decoder can handle different tasks in BMI system,including the detection of movement states and estimation of continuous kinematic parameters.
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.
文摘This review describes work presented in the 2014 inaugural Tsinghua University Press-Springer Nano Research Award lecture, as well as current and future opportunities for nanoscience research at the interface with brain science. First, we briefly summarize some of the considerations and the research journey that has led to our focus on bottom-up nanoscale science and technology. Second, we recapitulate the motivation for and our seminal contributions to nanowire- based nanoscience and technology, including the rational design and synthesis of increasingly complex nanowire structures, and the corresponding broad range of ＂applications＂ enabled by the capability to control structure, com- position and size from the atomic level upwards. Third, we describe in more detail nanowire-based electronic devices as revolutionary tools for brain science, including （i） motivation for nanoelectronics in brain science, （ii） demonstration of nanowire nanoelectronic arrays for high-spatial/high-temporal resolution extracellular recording, （iii） the development of fundamentally-new intracellular nanoelectronic devices that approach the sizes of single ion channels, （iv） the introduction and demonstration of a new paradigm for innervating cell networks with addressable nanoelectronic arrays in three-dimensions. Last, we conclude with a brief discussion of the exciting and potentially transformative advances expected to come from work at the nanoelectronics-brain interface.
基金supported by the National Basic Research Program of China (2013CB329504)
文摘This paper presents the first report of a system of human's speech interaction with rats via integration of brain–machine interfaces and automatic speech recognition technologies. We propose a novel human–rat speech interaction paradigm by incorporating speech translator module, which translates human's speech commands into suitable electrical brain stimulation to steer the rat to induce expected locomotor behaviors. The preliminary results show that we can guide a rat's movement by speech commands. We further look into the future application scenarios together with forthcoming challenges facing this newly evolved cyborg intelligent system. This work will pave the way for natural interaction with animal robots.
基金supported by the National Natural Science Foundation of China (61031002, 61001172)National High Technology Research and Development Program of China (2012AA011602, 2011CB504400)the Zhejiang Provincial Natural Science Foundation of China (Y2090707)
文摘Brain machine interfaces (BMIs) have demonstrated lots of successful arm-related reach decoding in past decades, which provide a new hope for restoring the lost motor functions for the disabled. On the other hand, the more sophisticated hand grasp movement, which is more fundamental and crucial for daily life, was less referred. Current state of arts has specified some grasp related brain areas and offline decoding results; however, online decoding grasp movement and real-time neuroprosthetic control have not been systematically investigated. In this study, we obtained neural data from the dorsal premotor cortex (PMd) when monkey reaching and grasping one of four differently shaped objects following visual cues. The four grasp gesture types with an additional resting state were classified asynchronously using a fuzzy k-nearest neighbor model, and an artificial hand was controlled online using a shared control strategy. The results showed that most of the neurons in PMd are tuned by reach and grasp movement, us- ing which we get a high average offline decoding accuracy of 97.1%. In the online demonstration, the instantaneous status of monkey grasping could be extracted successfully to control the artificial hand, with an event-wise accuracy of 85.1%. Overall, our results inspect the neural firing along the time course of grasp and for the first time enables asynchronous neural control of a prosthetic hand, which underline a feasible hand neural prosthesis in BMIs.
