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

Lateralization

Lateralization

Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.

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

Dual-Modulus Microcone Array for Graded Tactile Sensing and Intelligent Slip Detection.

ACS applied materials & interfaces·2026

Same author

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026

Same author

An Immersive P300 Brain-Computer Interface Based on 3D Morphological Stimuli and Self-Adaptive Bayesian Linear Discriminant Analysis.

Biomimetics (Basel, Switzerland)·2026

Same author

Advances in printable flexible and stretchable thin-film electrodes: materials, interfaces, technologies and bioelectronic applications.

Nanoscale·2026

Same author

Nondestructive determination of ash content in wheat flour via terahertz time-domain spectroscopy.

Frontiers in plant science·2026

Same author

Resting-state brain network alterations in adolescent idiopathic scoliosis using functional near-infrared spectroscopy.

Biomedical engineering online·2026

Search research articles

相关实验视频

Updated: May 10, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

一种基于侧面化指数的多层集成EEG通道选择方法.

Junhong Luo, Qing Liu, Pengrui Tai

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

|April 22, 2025

概括

此摘要是机器生成的。

一种新的方法,MLI-ECS-LI,优化了便携式脑机接口 (BCI) 的通道选择. 它提高了跨任务和主题的解码精度,提高了BCI的可用性和实际应用.

更多相关视频

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice

Published on: July 31, 2019

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

相关实验视频

Last Updated: May 10, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice

Published on: July 31, 2019

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

科学领域:

神经科学和生物医学工程
信号处理和机器学习

背景情况:

优化频道选择对于便携式脑电脑接口 (BCI) 技术至关重要.
在不牺牲解码精度的情况下减少电极数量是一个重大挑战.
现有的方法往往会增加计算负载,忽视跨主题通道选择.

研究的目的:

引入基于侧面化指数 (MLI-ECS-LI) 的新型多层集成EEG通道选择方法.
为了在机器图像BCI (MI-BCI) 中实现跨任务和跨主题场景的有效道选择.

主要方法:

开发了MLI-ECS-LI方法,使用横向化指数来识别重要的道.
时间和频率域特征从由MLI-ECS-LI.LI选择的频道中提取.
运动类型使用最小平方支持向量机 (LSSVM),随机森林 (RF) 和支持向量机 (SVM) 进行分类.

主要成果:

与传统方法相比,MLI-ECS-LI方法在各种场景中显示出更好的解码精度.
平均准确度的提高在2.8%至9.2%之间,取决于分类器和场景 (单任务,交叉任务,交叉主题).
在21名健康受试者中观察到显著的性能增长,突出显示了该方法的稳定性.

结论:

拟议的MLI-ECS-LI方法有效地减少了频道选择,同时保持或提高解码精度.
这种方法提高了便携式MI-BCI系统的实用性和实用性.
通过解决道选择中的关键限制,MLI-ECS-LI显示了现实世界BCI应用的巨大潜力.