Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Foundation Model-Based Zero-Shot Tissue Segmentation of Pathological Images via the Mixture of Local-to-Global Experts.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

EfficientCovNet: Modeling the Pairwise Voxel Dependency for Brain ROI Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

MoHD: Multi-mOdal survival prediction through Hierarchical Decoupling of whole-slide image pyramids and genomics.

Medical image analysis·2026

Same author

Functional system-specific brain aging across the Alzheimer's disease continuum.

Translational psychiatry·2026

Same author

Direct PET-to-CT Generation for Attenuation Correction: A Slice-to-Slice Continual Transformer Segmentation-Aware Network.

IEEE journal of biomedical and health informatics·2026

Same author

Shared genetic architecture between the topology of brain white matter structural connectome and fluid intelligence.

Communications biology·2026

Search research articles

相关实验视频

Updated: Jul 26, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

对基于EEG的大脑与计算机接口的运动轨迹重建进行了全面的审查.

Pengpai Wang¹, Xuhao Cao¹, Yueying Zhou¹

¹Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China.

Frontiers in neuroscience

|June 19, 2023

概括

本综述评估了基于脑电图 (EEG) 的脑电脑接口 (BCI) 方法来解码四肢运动轨迹. 它强调了神经康复和辅助策略的各种技术的性能.

关键词:

这是一个EEGEEGEEGEEGEEGEEGEEG.大脑-计算机接口接口动作执行动作执行运动图像,运动影像.轨道的重建和重建.

更多相关视频

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

Published on: July 29, 2009

相关实验视频

Last Updated: Jul 26, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

Published on: July 29, 2009

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
康复工程康复工程

背景情况:

大脑-计算机接口 (BCI) 融合了神经科学和计算机技术,用于神经康复.
肢体运动解码是BCI对运动障碍患者的研究的一个关键领域.

研究的目的:

审查和评估基于EEG的四肢轨迹解码方法.
确定当前解码技术的优缺点.
解决现有文献中缺乏综合性绩效评估的问题.

主要方法:

对肢体轨迹重建的实验范式的讨论.
对EEG预处理,特征提取和选择技术的分析.
评估各种解码算法和结果评估方法.

主要成果:

2D和3D四肢轨迹重建的解码方法的比较.
分析运动执行和运动图像解码之间的差异.
识别不同解码方法的优点和弱点.

结论:

基于EEG的肢体轨迹解码对辅助和康复技术具有重大前景.
需要进一步的研究来优化解码性能和解决开放挑战.
本综述为BCI驱动的神经康复的未来进展提供了基础.