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

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Neural Regeneration Research 2024年第3期19卷 663-670页

作者： Yang Guo Liying Sun Wenyao Zhong Nan Zhang Zongxuan Zhao Wen Tian Department of Hand Surgery Beijing Jishuitan HospitalBeijingChina

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 re

关键词： artificial intelligence artificial prosthesis medical-industrial integration brain-machine interface deep learning machine learning networked hand prosthesis neural interface neural network neural regeneration peripheral nerve

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Improvement of brain-machine interface Model on Dynamic Reaching Task

Improvement of Brain-Machine Interface Model on Dynamic Reac...

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第37届中国控制会议

作者： Jinggao Sun Rui Xue Hongguang Pan Jiaxiong Yang Shuo Wang Key Laboratory of Advanced Control and Optimization Technology of Ministry of Education East China University of Science and Technology School of Electrical and Control Engineering Xi'an University of Science and Technology

Based on the relative and required velocity integration to endpoint（RRVITE） model,we extend the neuroprosthetic model for voluntary single joint reaching task by adding the vector models related to relative speed information,which improves the model performance in dynamic reaching task.Through the synthetic simulation,we show that the extended model performs much better than the original one in motion prediction and steady state accuracy.Using synthetic data obtained through the simulation of the original model,a linear decoder based on Weiner filter has been trained and introduced into the improved model to test its performance.Both the evaluation results and the online test results show that the improved model can also enhance the performance of the BMI system with decoder.

关键词： brain-machine interface Neural Activity Model Weiner Decoder

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The auxiliary controller design based on model-free control in the brain-machine interface

The auxiliary controller design based on model-free control ...

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第30届中国控制与决策会议

作者： Hongguang Pan Wenyu Mi Mei Wang Jinggao Sun College of Electric and Control Engineering Xi'an University of Science and Technology Key Laboratory of Advanced Control and Optimization Technology of Ministry of Education East China University of Science and Technology

In this paper, a model-free control（MFC） method is used to design an artificial feedback to compensate the lack of natural feedback and achieve the reaching task for a voluntary single joint. Considering the MFC only depends on the I/O data of system, and does not need the system model, it can avoid the complex analysis on the brain model, and reduce the difficulty of controller design. Moreover, through choosing different pseudo-partial-derivative values, the influence of the pseudo-partialderivative value is analyzed and a series of control results are given;further, the computing efficiency is improved drastically through a comparison in the simulation.

关键词： auxiliary controller model-free control brain-machine interface

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

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Neuroscience Bulletin 2022年第7期38卷 796-808页

作者： Tianwei Wang Yun Chen He Cui Center for Excellence in brain Science and Intlligent Technology Institute of NeuroscienceChinese Academy of SciencesShanghai 200031China Shanghai Center for brain and brain-inspired Intelligence Technology Shanghai 200031China University of Chinese Academy of Sciences Beijing 100049China

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.

关键词： Dimensionality reduction Neural network machine learning Population decoding brain-machine interface

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

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Engineering 2020年第4期6卷 449-461页

作者： Zhaofei Yu Jian K.Liu Shanshan Jia Yichen Zhang Yajing Zheng Yonghong Tian Tiejun Huang National Engineering Laboratory for Video Technology School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Peng Cheng Laboratory Shenzhen 518055China Center for Systems Neuroscience Department of NeurosciencePsychology and BehaviourUniversity of LeicesterLeicester LE17RHUK

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.

关键词： Visual coding Retina Neuroprosthesis brain-machine interface Artificial intelligence Deep learning Spiking neural network Probabilistic graphical model

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Nonlinear Analysis Techniques of EEG and Their Applications to Anesthesia Depth Monitoring

Nonlinear Analysis Techniques of EEG and Their Applications...

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第2届世界华人生物医学工程研讨会

作者： Zheng Chongxun Xu jin Zhang Lianyi Institute of Biomedical Eng. Xi’an Jiaotong University Xi’an China 710049

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脑机融合技术的哲学审思

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科学技术哲学 2021年第3期

作者：张学义潘平平庄桂山

脑机融合是以脑机接口为基础技术，包括脑-机、机-脑、脑-脑等不同信息传输方式的技术统称。在理想状态下的脑机融合系统中，大脑与电脑等外部设备彼此互相适应、协同工作，大脑与大脑之间传递信息互通有无，实现生物脑的感知认知能力... 详细信息

脑机融合是以脑机接口为基础技术，包括脑-机、机-脑、脑-脑等不同信息传输方式的技术统称。在理想状态下的脑机融合系统中，大脑与电脑等外部设备彼此互相适应、协同工作，大脑与大脑之间传递信息互通有无，实现生物脑的感知认知能力与机器的高速计算、处理能力的完美结合。虽然该技术目前还停留在探索性试验阶段，但蕴含着广阔的应用前景和巨大的现实空间。在该技术的潜在图景还未完全实现之前，有必要进行深入的哲学审视：脑机融合融合了人-机-物，却有可能造成彼此类属关系的混乱，从而对人-机-物的本体论结构进行重构，同时引发价值体系的重塑和隐私的编码化、可视化，促使人类步入“超人类主义”与“后人类主义”时代。

关键词：脑机融合脑机接口混合智能系统数字化生存本体论重构　　brain-machine Integration brain-machine interface Cyborg Intelligent System Digital Subsistence Ontological Reconstruction

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