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用于皮层内脑机接口的神经解码

Neural Decoding for Intracortical Brain-Computer Interfaces.

作者信息

Dong Yuanrui, Wang Shirong, Huang Qiang, Berg Rune W, Li Guanghui, He Jiping

机构信息

School of Mechatronical Engineering and Beijing Advanced Innovation Center for Intelligent Robots, Beijing Institute of Technology, Beijing 100081, China.

Department of Neuroscience, University of Copenhagen, Copenhagen 2200, Denmark.

出版信息

Cyborg Bionic Syst. 2023 Jul 28;4:0044. doi: 10.34133/cbsystems.0044. eCollection 2023.

Abstract

Brain-computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life. To accurately and stably control effectors, it is important for decoders to recognize an individual's motor intention from neural activity either by noninvasive or intracortical neural recording. Intracortical recording is an invasive way of measuring neural electrical activity with high temporal and spatial resolution. Herein, we review recent developments in neural signal decoding methods for intracortical brain-computer interfaces. These methods have achieved good performance in analyzing neural activity and controlling robots and prostheses in nonhuman primates and humans. For more complex paradigms in motor rehabilitation or other clinical applications, there remains more space for further improvements of decoders.

摘要

脑机接口通过为瘫痪患者提供控制外部设备和改善日常生活质量的解决方案,彻底改变了神经科学领域。为了准确、稳定地控制效应器,解码器通过非侵入性或皮层内神经记录从神经活动中识别个体的运动意图非常重要。皮层内记录是一种以高时间和空间分辨率测量神经电活动的侵入性方法。在此,我们综述了用于皮层内脑机接口的神经信号解码方法的最新进展。这些方法在分析神经活动以及控制非人灵长类动物和人类的机器人及假肢方面取得了良好的性能。对于运动康复或其他临床应用中更复杂的范例,解码器仍有更大的改进空间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81ae/10380541/8058ba60b55c/cbsystems.0044.fig.001.jpg

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