Brain-computer interface(BCI)technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices.The efficacy of BCI systems is largely contingent...Brain-computer interface(BCI)technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices.The efficacy of BCI systems is largely contingent upon the progress in signal acquisition methodologies.This paper endeavors to provide an exhaustive synopsis of signal acquisition technologies within the realm of BCI by scrutinizing research publications from the last ten years.Our review synthesizes insights from both clinical and engineering viewpoints,delineating a comprehensive two-dimensional framework for understanding signal acquisition in BCIs.We delineate nine discrete categories of technologies,furnishing exemplars for each and delineating the salient challenges pertinent to these modalities.This review furnishes researchers and practitioners with a broad-spectrum comprehension of the signal acquisition landscape in BCI,and deliberates on the paramount issues presently confronting the field.Prospective enhancements in BCI signal acquisition should focus on harmonizing a multitude of disciplinary perspectives.Achieving equilibrium between signal fidelity,invasiveness,biocompatibility,and other pivotal considerations is imperative.By doing so,we can propel BCI technology forward,bolstering its effectiveness,safety,and depend-ability,thereby contributing to an auspicious future for human-technology integration.展开更多
With the advancing of industrialization and the advent of the information age, intelligent robots play an increasingly important role in intelligent manufacturing, intelligent transportation system, the Internet of th...With the advancing of industrialization and the advent of the information age, intelligent robots play an increasingly important role in intelligent manufacturing, intelligent transportation system, the Internet of things, medical health and intelligent services. Based on working experiences in and reviews on intelligent robot studies both in China and abroad, the authors summarized researches on key and leading technologies related to human-robot collaboration, driverless technology, emotion recognition, brain-computer interface, bionic software robot and cloud platform, big data network, etc. The development trend of intelligent robot was discussed, and reflections on and suggestions to intelligent robot development in China were proposed. The review is not only meant to overview leading technologies of intelligent robot all over the world, but also provide related theories, methods and technical guidance to the technological and industrial development of intelligent robot in China.展开更多
Spinal cord injury is linked to the interruption of neural pathways,which results in irreversible neural dysfunction.Neural repair and neuroregeneration are critical goals and issues for rehabilitation in spinal cord ...Spinal cord injury is linked to the interruption of neural pathways,which results in irreversible neural dysfunction.Neural repair and neuroregeneration are critical goals and issues for rehabilitation in spinal cord injury,which require neural stem cell repair and multimodal neuromodulation techniques involving personalized rehabilitation strategies.Besides the involvement of endogenous stem cells in neurogenesis and neural repair,exogenous neural stem cell transplantation is an emerging effective method for repairing and replacing damaged tissues in central nervous system diseases.However,to ensure that endogenous or exogenous neural stem cells truly participate in neural repair following spinal cord injury,appropriate interventional measures(e.g.,neuromodulation)should be adopted.Neuromodulation techniques,such as noninvasive magnetic stimulation and electrical stimulation,have been safely applied in many neuropsychiatric diseases.There is increasing evidence to suggest that neuromagnetic/electrical modulation promotes neuroregeneration and neural repair by affecting signaling in the nervous system;namely,by exciting,inhibiting,or regulating neuronal and neural network activities to improve motor function and motor learning following spinal cord injury.Several studies have indicated that fine motor skill rehabilitation training makes use of residual nerve fibers for collateral growth,encourages the formation of new synaptic connections to promote neural plasticity,and improves motor function recovery in patients with spinal cord injury.With the development of biomaterial technology and biomechanical engineering,several emerging treatments have been developed,such as robots,brain-computer interfaces,and nanomaterials.These treatments have the potential to help millions of patients suffering from motor dysfunction caused by spinal cord injury.However,large-scale clinical trials need to be conducted to validate their efficacy.This review evaluated the efficacy of neural stem cells and magnetic or electrical stimulation combined with rehabilitation training and intelligent therapies for spinal cord injury according to existing evidence,to build up a multimodal treatment strategy of spinal cord injury to enhance nerve repair and regeneration.展开更多
Objective:Behavioral interventions have been shown to ameliorate the electroencephalogram(EEG)dynamics underlying the behavioral symptoms of autism spectrum disorder(ASD),while studies have also demonstrated that mirr...Objective:Behavioral interventions have been shown to ameliorate the electroencephalogram(EEG)dynamics underlying the behavioral symptoms of autism spectrum disorder(ASD),while studies have also demonstrated that mirror neuron mu rhythm-based EEG neurofeedback training improves the behavioral functioning of individuals with ASD.This study aimed to test the effects of a wearable mu rhythm neurofeedback training system based on machine learning algorithms for children with autism.Methods:A randomized,placebo-controlled study was carried out on 60 participants aged 3 to 6 years who were diagnosed with autism,at two center-based intervention sites.The neurofeedback group received active mu rhythm neurofeedback training,while the control group received a sham neurofeedback training.Other behavioral intervention programs were similar between the two groups.Results:After 60 sessions of treatment,both groups showed significant improvements in several domains including language,social and problem behavior.The neurofeedback group showed significantly greater improvements in expressive language(P=0.013)and cognitive awareness(including joint attention,P=0.003)than did the placebo-controlled group.Conclusion:Artificial intelligence-powered wearable EEG neurofeedback,as a type of brain-computer interface application,is a promising assistive technology that can provide targeted intervention for the core brain mechanisms underlying ASD symptoms.展开更多
Electroencephalogram(EEG)data depict various emotional states and reflect brain activity.There has been increasing interest in EEG emotion recognition in brain-computer interface systems(BCIs).In the World Robot Conte...Electroencephalogram(EEG)data depict various emotional states and reflect brain activity.There has been increasing interest in EEG emotion recognition in brain-computer interface systems(BCIs).In the World Robot Contest(WRC),the BCI Controlled Robot Contest successfully staged an emotion recognition technology competition.Three types of emotions(happy,sad,and neutral)are modeled using EEG signals.In this study,5 methods employed by different teams are compared.The results reveal that classical machine learning approaches and deep learning methods perform similarly in offline recognition,whereas deep learning methods perform better in online cross-subject decoding.展开更多
The modern medical field faces two critical challenges:the dramatic increase in data complexity and the explosive growth in data size.Especially in current research,medical diagnostic,and data processing devices relyi...The modern medical field faces two critical challenges:the dramatic increase in data complexity and the explosive growth in data size.Especially in current research,medical diagnostic,and data processing devices relying on traditional computer architecture are increasingly showing limitations when faced with dynamic temporal and spatial processing requirements,as well as high-dimensional data processing tasks.Neuromorphic devices provide a new way for biomedical data processing due to their low energy consumption and high dynamic information processing capabilities.This paper aims to reveal the advantages of neuromorphic devices in biomedical applications.First,this review emphasizes the urgent need of biomedical engineering for diversify clinical diagnostic techniques.Secondly,the feasibility of the application in biomedical engineering is demonstrated by reviewing the historical development of neuromorphic devices from basic modeling to multimodal signal processing.In addition,this paper demonstrates the great potential of neuromorphic chips for application in the fields of biosensing technology,medical image processing and generation,rehabilitation medical engineering,and brain-computer interfaces.Finally,this review provides the pathways for constructing standardized experimental protocols using biocompatible technologies,personalized treatment strategies,and systematic clinical validation.In summary,neuromorphic devices will drive technological innovation in the biomedical field and make significant contributions to life health.展开更多
The emerging field of affective computing focuses on enhancing computers’ability to understand and appropriately respond to people’s affective states in human-computer interactions,and has revealed significant poten...The emerging field of affective computing focuses on enhancing computers’ability to understand and appropriately respond to people’s affective states in human-computer interactions,and has revealed significant potential for a wide spectrum of applications.Recently,the electroencephalography(EEG)based affective computing has gained increasing interest for its good balance between mechanistic exploration and real-world practical application.The present work reviewed ten theoretical and operational challenges for the existing affective computing researches from an interdisciplinary perspective of information technology,psychology,and neuroscience.On the theoretical side,we suggest that researchers should be well aware of the limitations of the commonly used emotion models,and be cautious about the widely accepted assumptions on EEG-emotion relationships as well as the transferability of findings based on different research paradigms.On the practical side,we propose several operational recommendations for the challenges about data collection,feature extraction,model implementation,online system design,as well as the potential ethical issues.The present review is expected to contribute to an improved understanding of EEG-based affective computing and promote further applications.展开更多
基金supported by the National Natural Science Foun-dation of China(U2241208,62171473,61671424)the National Key Research and Development Program of China(2022YFC3602803,2023YFF1205300)Key Research and Development Program of Ningxia(2023BEG02063)。
文摘Brain-computer interface(BCI)technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices.The efficacy of BCI systems is largely contingent upon the progress in signal acquisition methodologies.This paper endeavors to provide an exhaustive synopsis of signal acquisition technologies within the realm of BCI by scrutinizing research publications from the last ten years.Our review synthesizes insights from both clinical and engineering viewpoints,delineating a comprehensive two-dimensional framework for understanding signal acquisition in BCIs.We delineate nine discrete categories of technologies,furnishing exemplars for each and delineating the salient challenges pertinent to these modalities.This review furnishes researchers and practitioners with a broad-spectrum comprehension of the signal acquisition landscape in BCI,and deliberates on the paramount issues presently confronting the field.Prospective enhancements in BCI signal acquisition should focus on harmonizing a multitude of disciplinary perspectives.Achieving equilibrium between signal fidelity,invasiveness,biocompatibility,and other pivotal considerations is imperative.By doing so,we can propel BCI technology forward,bolstering its effectiveness,safety,and depend-ability,thereby contributing to an auspicious future for human-technology integration.
基金supported by the Chinese MIIT Intelligent Manufacturing and New Mode Application "Application of new mode of intelligent manufacturing of Chinese medicine products"
文摘With the advancing of industrialization and the advent of the information age, intelligent robots play an increasingly important role in intelligent manufacturing, intelligent transportation system, the Internet of things, medical health and intelligent services. Based on working experiences in and reviews on intelligent robot studies both in China and abroad, the authors summarized researches on key and leading technologies related to human-robot collaboration, driverless technology, emotion recognition, brain-computer interface, bionic software robot and cloud platform, big data network, etc. The development trend of intelligent robot was discussed, and reflections on and suggestions to intelligent robot development in China were proposed. The review is not only meant to overview leading technologies of intelligent robot all over the world, but also provide related theories, methods and technical guidance to the technological and industrial development of intelligent robot in China.
基金supported by the Major International(Regional)Joint Research Project of the National Natural Science Foundation of China,No.81820108013(to LMC)the General Research Project of the National Natural Science Foundation of China,No.81772453(to DSX)the National Key Research and Development Program of China,No.2016YFA0100800(to LMC)
文摘Spinal cord injury is linked to the interruption of neural pathways,which results in irreversible neural dysfunction.Neural repair and neuroregeneration are critical goals and issues for rehabilitation in spinal cord injury,which require neural stem cell repair and multimodal neuromodulation techniques involving personalized rehabilitation strategies.Besides the involvement of endogenous stem cells in neurogenesis and neural repair,exogenous neural stem cell transplantation is an emerging effective method for repairing and replacing damaged tissues in central nervous system diseases.However,to ensure that endogenous or exogenous neural stem cells truly participate in neural repair following spinal cord injury,appropriate interventional measures(e.g.,neuromodulation)should be adopted.Neuromodulation techniques,such as noninvasive magnetic stimulation and electrical stimulation,have been safely applied in many neuropsychiatric diseases.There is increasing evidence to suggest that neuromagnetic/electrical modulation promotes neuroregeneration and neural repair by affecting signaling in the nervous system;namely,by exciting,inhibiting,or regulating neuronal and neural network activities to improve motor function and motor learning following spinal cord injury.Several studies have indicated that fine motor skill rehabilitation training makes use of residual nerve fibers for collateral growth,encourages the formation of new synaptic connections to promote neural plasticity,and improves motor function recovery in patients with spinal cord injury.With the development of biomaterial technology and biomechanical engineering,several emerging treatments have been developed,such as robots,brain-computer interfaces,and nanomaterials.These treatments have the potential to help millions of patients suffering from motor dysfunction caused by spinal cord injury.However,large-scale clinical trials need to be conducted to validate their efficacy.This review evaluated the efficacy of neural stem cells and magnetic or electrical stimulation combined with rehabilitation training and intelligent therapies for spinal cord injury according to existing evidence,to build up a multimodal treatment strategy of spinal cord injury to enhance nerve repair and regeneration.
基金funded by a grant from Qiangnao Keji(BrainCo)Ltd.
文摘Objective:Behavioral interventions have been shown to ameliorate the electroencephalogram(EEG)dynamics underlying the behavioral symptoms of autism spectrum disorder(ASD),while studies have also demonstrated that mirror neuron mu rhythm-based EEG neurofeedback training improves the behavioral functioning of individuals with ASD.This study aimed to test the effects of a wearable mu rhythm neurofeedback training system based on machine learning algorithms for children with autism.Methods:A randomized,placebo-controlled study was carried out on 60 participants aged 3 to 6 years who were diagnosed with autism,at two center-based intervention sites.The neurofeedback group received active mu rhythm neurofeedback training,while the control group received a sham neurofeedback training.Other behavioral intervention programs were similar between the two groups.Results:After 60 sessions of treatment,both groups showed significant improvements in several domains including language,social and problem behavior.The neurofeedback group showed significantly greater improvements in expressive language(P=0.013)and cognitive awareness(including joint attention,P=0.003)than did the placebo-controlled group.Conclusion:Artificial intelligence-powered wearable EEG neurofeedback,as a type of brain-computer interface application,is a promising assistive technology that can provide targeted intervention for the core brain mechanisms underlying ASD symptoms.
基金This work was supported in part by the National Natural Science Foundation of China(Grant Nos.U21A20485,61976175).
文摘Electroencephalogram(EEG)data depict various emotional states and reflect brain activity.There has been increasing interest in EEG emotion recognition in brain-computer interface systems(BCIs).In the World Robot Contest(WRC),the BCI Controlled Robot Contest successfully staged an emotion recognition technology competition.Three types of emotions(happy,sad,and neutral)are modeled using EEG signals.In this study,5 methods employed by different teams are compared.The results reveal that classical machine learning approaches and deep learning methods perform similarly in offline recognition,whereas deep learning methods perform better in online cross-subject decoding.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(Grant No.12425209)the National Natural Science Foundation of China(11827803,12172034,62004056,62104058,62271269).
文摘The modern medical field faces two critical challenges:the dramatic increase in data complexity and the explosive growth in data size.Especially in current research,medical diagnostic,and data processing devices relying on traditional computer architecture are increasingly showing limitations when faced with dynamic temporal and spatial processing requirements,as well as high-dimensional data processing tasks.Neuromorphic devices provide a new way for biomedical data processing due to their low energy consumption and high dynamic information processing capabilities.This paper aims to reveal the advantages of neuromorphic devices in biomedical applications.First,this review emphasizes the urgent need of biomedical engineering for diversify clinical diagnostic techniques.Secondly,the feasibility of the application in biomedical engineering is demonstrated by reviewing the historical development of neuromorphic devices from basic modeling to multimodal signal processing.In addition,this paper demonstrates the great potential of neuromorphic chips for application in the fields of biosensing technology,medical image processing and generation,rehabilitation medical engineering,and brain-computer interfaces.Finally,this review provides the pathways for constructing standardized experimental protocols using biocompatible technologies,personalized treatment strategies,and systematic clinical validation.In summary,neuromorphic devices will drive technological innovation in the biomedical field and make significant contributions to life health.
基金supported by National Science Foundation of China under Grant U1736220MOE(Ministry of Education China)Project of Humanities and Social Sciences(17YJA190017)+1 种基金National Social Science Foundation of China under Grant 17ZDA323National Key Research and Development Plan under Grant 2016YFB1001200.
文摘The emerging field of affective computing focuses on enhancing computers’ability to understand and appropriately respond to people’s affective states in human-computer interactions,and has revealed significant potential for a wide spectrum of applications.Recently,the electroencephalography(EEG)based affective computing has gained increasing interest for its good balance between mechanistic exploration and real-world practical application.The present work reviewed ten theoretical and operational challenges for the existing affective computing researches from an interdisciplinary perspective of information technology,psychology,and neuroscience.On the theoretical side,we suggest that researchers should be well aware of the limitations of the commonly used emotion models,and be cautious about the widely accepted assumptions on EEG-emotion relationships as well as the transferability of findings based on different research paradigms.On the practical side,we propose several operational recommendations for the challenges about data collection,feature extraction,model implementation,online system design,as well as the potential ethical issues.The present review is expected to contribute to an improved understanding of EEG-based affective computing and promote further applications.
