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有效康复的神经编码

Neural coding for effective rehabilitation.

作者信息

Hu Xiaoling, Wang Yiwen, Zhao Ting, Gunduz Aysegul

机构信息

Interdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.

Qiushi Academy for Advanced Studies, Zhejiang University, Zhejiang 310027, China.

出版信息

Biomed Res Int. 2014;2014:286505. doi: 10.1155/2014/286505. Epub 2014 Sep 2.

Abstract

Successful neurological rehabilitation depends on accurate diagnosis, effective treatment, and quantitative evaluation. Neural coding, a technology for interpretation of functional and structural information of the nervous system, has contributed to the advancements in neuroimaging, brain-machine interface (BMI), and design of training devices for rehabilitation purposes. In this review, we summarized the latest breakthroughs in neuroimaging from microscale to macroscale levels with potential diagnostic applications for rehabilitation. We also reviewed the achievements in electrocorticography (ECoG) coding with both animal models and human beings for BMI design, electromyography (EMG) interpretation for interaction with external robotic systems, and robot-assisted quantitative evaluation on the progress of rehabilitation programs. Future rehabilitation would be more home-based, automatic, and self-served by patients. Further investigations and breakthroughs are mainly needed in aspects of improving the computational efficiency in neuroimaging and multichannel ECoG by selection of localized neuroinformatics, validation of the effectiveness in BMI guided rehabilitation programs, and simplification of the system operation in training devices.

摘要

成功的神经康复取决于准确的诊断、有效的治疗和定量评估。神经编码作为一种解释神经系统功能和结构信息的技术,推动了神经成像、脑机接口(BMI)以及康复训练设备设计等领域的发展。在本综述中,我们总结了从微观到宏观层面神经成像的最新突破及其在康复诊断中的潜在应用。我们还回顾了在动物模型和人类中用于BMI设计的皮层脑电图(ECoG)编码、用于与外部机器人系统交互的肌电图(EMG)解读以及机器人辅助的康复计划进展定量评估方面所取得的成果。未来的康复将更多地以家庭为基础、自动化且由患者自助完成。在通过选择局部神经信息学提高神经成像和多通道ECoG的计算效率、验证BMI引导康复计划的有效性以及简化训练设备的系统操作等方面,主要还需要进一步的研究和突破。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03a4/4167232/63eaf926be90/BMRI2014-286505.001.jpg

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