Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

Electrode materials for brain-machine interface:A review

Brain-machine interface(BMI)is a device that translates neuronal information into commands,which is capable of controlling external software or hardware,such as a computer or robotic arm.In consequence,the electrodes with desirable electrical and mechanical properties for direct interacting between neural tissues and machines serves as the crucial and critical part of BMI technology.Nowadays,the development of material science provides many advanced electrodes for neural stimulating and recording.Particularly,the widespread applications of nanotechnologies have innovatively introduced biocompatible electrode that can have similar characteristics with neural tissue.This paper reviews the existing problems and discusses the latest development of electrode materials for BMI,including conducting polymers,silicon,carbon nanowires,graphene,and hybrid organic-inorganic nanomaterials.In addition,we will inspect at the technical and scientific challenges in the development of neural electrode for a broad application of BMI with focus on the biocompatibility,mechanical mismatch,and electrical performance of electrode materials.

直接脑控机器人接口技术

直接脑控机器人接口(Brain-controlled robot interface,BCRI)是一种新型的人-机器人接口技术,是脑-机器接口/脑-计算机接口(Brain-machine interface,BMI/Brain-computer interface,BCI)在机器人控制领域的重要应用和研究方向.研究者相继在Nature、Science和其他重要国际期刊上报道了相关的实验研究和开发,目前已成为国际前沿研究热点.本文主要围绕BCRI中的控制策略、BMI/BCI模块与机器人多层控制模块的适应和融合、BCRI中的脑信号自适应分类算法以及人、BMI/BCI模块和机器人控制系统的三边自适应展开论述,分析了目前的研究情况、存在的局限和面临的若干重要问题,指出进一步的研究思路和方向.

动物机器人新型智能监控系统设计

利用USB通信和无线通信,设计了新型动物机器人智能监控系统。系统由PC监控软件、信号发射器、接收控制器等部分组成。与以往系统相比,该系统不仅可实现动物机器人行为控制,还可实现行为监测以及环境数据实时采集,具有微体积、微功耗等特点。经动物实验表明,该系统通信可靠、工作时间长、控制准确、操作方便、数据采集精确,将为动物机器人的实时监控提供有力的技术支持。

偶联剂对硼酸铝晶须/双马来酰亚胺性能的影响

应用自行合成的一类新型硼酸酯偶联剂及硅烷偶联剂等对硼酸铝晶须进行表面处理,考察了硼酸铝晶须对双马来酰亚胺树脂体系性能的影响.结果表明,硼酸酯处理后的晶须对材料的改性作用较硅烷更加显著;硼酸铝晶须添加到双马来酰亚胺树脂中后,材料的弯曲强度在晶须含量为5%时达到最大值,而后随晶须含量的增大稍有下降;随晶须添加量的增大材料的弯曲模量和耐热性逐渐提高;经硼酸酯处理的晶须与树脂基体具有更好的界面粘接.

直接脑-机接口生物相容性研究进展

脑-机接口的用途是为严重瘫痪病人提供一个与外部世界进行通信的出口。根据检测电极的不同,分为间接型和直接型脑-机接口。直接型脑-机接口是将检测电极植入大脑皮质内,可反映较小范围内神经元细胞的电活动,避免信号传递引起的衰减和其它信号的干扰,从而满足精细控制所要求的时空分辨率和信噪比。直接型脑-机接口研究的关键问题是提高直接脑-机接口电极的生物相容性。本文介绍了具有较高生物相容性的直接脑-机接口电极的技术特点。

基于偏最小二乘的大鼠初级运动皮层解码

为了准确解析大鼠初级运动皮层神经元发放模式并预测相应的肢体动作,实验同时记录大鼠初级运动皮层神经元峰电位发放和大鼠前肢压力,利用K均值法和主成分分析法对神经元进行分类,采用偏最小二乘分析大鼠初级运动皮层神经元活动与前肢运动参数之间的关系,并对该解码结果与维纳滤波和卡尔曼滤波算法的解码结果进行比较.实验结果表明:神经元的发放活动在压杆前0.6s开始有增加的趋势,提示大鼠的初级运动皮层神经元分布式活动可用于大鼠前肢运动的解析和预测,且偏最小二乘解码得到的预测值与真实压杆值的相关系数在0.85以上,均高于维纳滤波和卡尔曼滤波的解码结果.

“人机耦合”型H-M系统关键技术——BCI研究

论述了人机一体化系统的概念和三种控制类型,提出大脑计算机接口(B rain-Computer Interface,BC I)是“人机耦合”型H-M系统关键技术。阐述了BC I的研究起源、脑电信号的分析方法、BC I的6种研究方法,BC I系统的硬件构成,对BC I的研究和人机一体化系统和技术的发展具有重要意义。

基于干电极的头带式射频无线脑-机接口系统

研发了一种采用干电极的脑-机接口系统，此系统克服了现有脑机接口用脑电信号采集及信号处理系统笨重而不便于携带的缺点。此系统采用半导体微加工工艺制作的“干”电极作为采集脑电信号的电极，佩戴方便且能长时间使用。整个便携式脑机接口系统在脑电信号采集、处理和传输三个方面都采用了利于便携的设计，信号处理采用专用的集成电路，信号的采集和处理端与信号接收端采用射频芯片收发数据。整个系统的重量只有39g。该脑-机接口系统以人体的专注程度作为控制外部设备的控制信号，经过训练的受试者可以通过脑电信号实现对机器的控制。

多通道实时神经信号采集与峰电位检测系统

研制了一种可应用于在体神经检测的新型多通道实时神经信号采集与峰电位检测系统。该系统主要包括自主研发的植入式硅基微电极、集成多通道神经信号放大电路以及模数转换器(ADC)和数字信号处理器(DSP)等部分商用器件。该系统采用局部非线性能量算子(NEO)对采集到的32通道神经信号进行同步峰电位检测,并在终端屏幕上显示检测结果。局部NEO峰电位检测算法硬件实现简单,能够完全自主地对神经信号峰电位进行实时检测。在生理盐水模拟的在体实验环境中的测试表明,整个系统的信噪比(SNR)为27.4dB,在此信噪比的条件下,局部NEO峰电位检测算法的检测精度可达95%。

Novel Biological Based Method for Robot Navigation and Localization

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.

Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body.Incoming visual information can be processed by the brain in millisecond intervals.The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation.Thus,the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike.Closed-loop computation in a neuroprosthesis includes two stages:encoding a stimulus as a neuronal signal,and decoding it back into a stimulus.In this paper,we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos.We hypothesize that in order to obtain a better understanding of the computational principles in the retina,a hypercircuit view of the retina is necessary,in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina.The different building blocks of the retina,which include a diversity of cell types and synaptic connections-both chemical synapses and electrical synapses(gap junctions)-make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes.An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.

脑机交互研究及标准化实践

介绍脑机交互的概念及标准化的必要性,讨论近年来脑机交互的发展状况,重点阐述国内外植入式脑机接口的研究历程,并从脑机交互系统角度提出了脑机交互标准化的思考,同时介绍在脑机交互计算标准化方面的相关实践。

Odorant discrimination using functional near-infrared spectroscopy of the main olfactory bulb in rats

We characterize the hemodynamic response changes in the main olfactory bulb(MOB)of anesthetized rats with near-infrared spectroscopy(NIRS)during the presentation of three different odorants:(i)plain air as a reference(Blank),(ii) 2-heptanone(HEP),and(iii)isopropylbenzene(Ib).Odorants generate different changes in the concentrations of oxyhemoglobin.Our results suggest that NIRS technology might be useful in discriminating various odorants in a non-invasive manner using animals with a superb olfactory system.

Finger movement inference using M1 neural activities

The paper presents the neural decoding result of finger or wrist movements using the primary motor cortex(M1)neural activities prior to its movement.It is well known that the observations of motor commands in brain are in advance before motor movements in the central nerve system.Readiness potential(RP)for electroencephalogram(EEG)has become an important domain of research.Likewise,pre-movement neural responses in M1 primary motor cortex have been observed.The neural activity data before 1 s.were used for neural decoding when the actual movements happened around 1 s.The obtained decoding accuracy in novel method reaches as high as 95% with 30 randomly selected neurons.

碳纤维复合材料短梁剪切疲劳特性

研究了碳纤维增强双马来酰亚胺复合材料的层间剪切静态与疲劳加载性质,分析了不同基体、不同铺层界面对层剪性质的影响。给出了层剪疲劳加载的S-N曲线;讨论了层间剪切损伤和增强机理,观察了微观特征。与碳/环氧复合材料相比,双马来酰亚胺基体复合材料在碳纤维和基体之间粘接良好,从而改进了纤维抗疲劳裂纹扩展的能力。

基于遗传算法LS-SVM直接逆模型的闭环脑机接口单关节控制

脑机接口系统通过大脑—计算机接口技术和控制理论的组合来弥补由于肌体的受损部分而造成的信息缺失.本研究基于心理生理皮质神经元放电率电路模型,在脑机接口控制理论分析的基础上进行自发单关节运动任务,采用自适应ESN(echo state network)设计非线性解码器,并引入FORCE(First Order Reduced and Contrdled Error learning)算法更新网络输出权值,通过仿真有无自然本体反馈信息情况下的解码器的性能来验证所设计的解码器的有效性.最后,通过基于遗传算法LS-SVM(least squares support vector machine)的直接逆模型框架,设计近似大脑皮层感觉区神经元放电率的最佳人工本体反馈去刺激大脑皮层感觉区神经元.仿真结果发现,所设计的闭环脑机接口(BMI)系统框架能够很好地恢复在线自发单关节自然运动任务性能,这也为当系统模型未知时,根据对象的输入输出数据恢复闭环系统的性能提供了新的研究思路.

From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control

In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preferenceforkinetics andkinematics,a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution.In this review,we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view.Here,we aim to reconcile the above perspectives,and evaluate their theoretical impact,future direction,and potential applications in brain-machine interfaces.

Speech interaction with a rat

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.

Neural decoding based on probabilistic neural network

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.