Jove
Visualize
联系我们

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

Lightweight deep learning models for EEG decoding: a review.

Journal of neural engineering·2025
Same author

Augmenting Electroencephalogram Transformer for Steady-State Visually Evoked Potential-Based Brain-Computer Interfaces.

Cyborg and bionic systems (Washington, D.C.)·2025
Same author

Dataset of binocularly coded steady-state visual evoked potentials recorded with an augmented reality headset.

Scientific data·2025
Same author

Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain-Computer Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same author

Corrigendum to "Sendai virus-based immunoadjuvant in hydrogel vaccine intensity-modulated dendritic cells activation for suppressing tumorigenesis" [Bioact. Mater. 6 (2021) 3879-3891].

Bioactive materials·2025
Same author

Enhanced theta oscillations in the left temporoparietal region associated with refractory positive symptoms in schizophrenia.

Schizophrenia (Heidelberg, Germany)·2025
Same journal

Spatiotemporally distinctive astrocytic and neuronal responses to repetitive intracortical microstimulation.

Journal of neural engineering·2026
Same journal

A neural mass modelling framework for evaluating EEG source localisation of seizure activity.

Journal of neural engineering·2026
Same journal

Functional and effective connectivity methods from SEEG for characterizing epileptogenic networks in refractory epilepsy: a comprehensive review and future directions.

Journal of neural engineering·2026
Same journal

Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks.

Journal of neural engineering·2026
Same journal

The seizure embedding map: A spatio-temporal transformer for comparing patients by ictal intracranial EEG features at scale.

Journal of neural engineering·2026
Same journal

Decoding imagined Chinese speech: A capsule neural network based on bidirectional knowledge transfer for hierarchical multi-label classification.

Journal of neural engineering·2026
查看所有相关文章
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jan 16, 2026

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy
07:21

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Published on: June 27, 2025

435

BGTransform:一个以神经生理学为基础的EEG数据增强框架.

Jin Yue1, Xiaolin Xiao1,2, Hao Zhang1,2

  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China.

Journal of neural engineering
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

一种名为背景EEG转换 (BGTransform) 的新方法增强了脑电图 (EEG) 大脑计算机接口 (BCI) 模型. 它通过增强数据来提高准确性和稳定性,同时保留关键的神经生理信号.

关键词:
这就是BCI的意义.这是一个EEGEEGEEGEEGEEGEEGEEG.背景 EEG 的背景数据增强数据增强

更多相关视频

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

3.0K
Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

26.7K

相关实验视频

Last Updated: Jan 16, 2026

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy
07:21

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Published on: June 27, 2025

435
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

3.0K
Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

26.7K

科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 生物医学工程 生物医学工程

背景情况:

  • 深度学习显示了基于脑电图 (EEG) 的脑电脑接口 (BCI) 信号解码的前景.
  • 数据稀缺性和可变性限制了BCI中的深度学习模型性能.
  • 现有的数据增强方法可能会扭曲信号或缺乏生理学有效性.

研究的目的:

  • 引入一个新的数据增强策略,BGTransform,以改善EEG-BCI概括.
  • 在增强过程中保持EEG信号的神经生理结构.
  • 为了解决培训BCI深度学习模型的数据稀疏性挑战.

主要方法:

  • 拟议的背景EEG转换 (BGTransform),一个利用任务相关活动和背景EEG之间的神经生理学分离的框架.
  • 通过扰乱背景EEG而产生新的试验,同时保留与任务相关的信号.
  • 将BGTransform应用于三个公共EEG-BCI数据集 (SSVEP和P300),并使用各种神经解码模型进行评估.

主要成果:

  • 在数据集和架构中,BGTransform的表现始终优于基线模型和传统增强技术.
  • 与没有BGTransform的模型相比,实现了2.45%至17.15%的平均分类准确度改进.
  • 在不同主题,任务和不同记录条件下,表现出增强的稳定性和稳定的性能.

结论:

  • BGTransform提供了一个原则性的,神经生理学知情的方法来增强EEG数据.
  • 通过引入受控的可变性,同时保留歧视性特征,有效地解决数据稀疏性问题.
  • 支持BGTransform的实用性,用于提高神经工程中的深度学习模型的准确性,稳定性和通用性.