Jove
Visualize
联系我们
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: Sep 18, 2025

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.3K

从fNIRS数据中对工作负载预测进行区块智能域调整.

Jiyang Wang1, Ayse Altay1, Leanne Hirshfield2

  • 1Electrical Engineering and Computer Science Department, Syracuse University, Syracuse, NY 13244, USA.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
概括
此摘要是机器生成的。

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

Misinformation research needs ecological validity.

Nature human behaviour·2024
Same author

Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification.

Journal of transportation engineering. Part A, Systems·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

使用功能近红外光谱学 (fNIRS) 预测认知工作量,通过一种新的区块智能域适应方法得到了改进. 这种方法增强了对实体应用的跨学科和课程的模型概括性.

科学领域:

  • 神经科学是一个神经科学.
  • 认知科学 认知科学
  • 生物医学工程 生物医学工程

背景情况:

  • 功能近红外光谱 (fNIRS) 测量皮质血液动力活动的非侵入性.
  • 从fNIRS数据中预测认知工作量面临着跨学科和会议概括的挑战.
  • 现有的方法往往无法在未见的受试者身上表现良好,原因是受试者之间和受试者内部数据的高变化.

研究的目的:

  • 从fNIRS数据中开发一种可靠的方法来预测认知工作量,这种方法可以在各个科目和会话中进行概括.
  • 为了应对fNIRS数据中主体间和主体内变异性的挑战.
  • 提高认知工作负载预测模型在现实环境中的适用性.

主要方法:

  • 提出了一个区块智能域调整 (BWise-DA) 方法,通过将同一主体/会话的区块视为不同的域来最小化会话内部变异.
  • 最小化类内部域差异和最大化类间域差异.
  • 介绍了一种基于MLPMixer的模型用于工作负载预测和对比学习方法.

主要成果:

  • 拟议的BWise-DA方法和MLPMixer模型在三个公共工作负载数据集 (n-back和finger-tapping任务) 上表现优于三个基线模型.
  • 对比式学习方法提高了基线模型的性能.
关键词:
有关认知性的认知.相反的学习学习学习.域名适应 域名适应在FNIRS中使用.工作负载的工作负载.

更多相关视频

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

1.2K
fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

21.0K

相关实验视频

Last Updated: Sep 18, 2025

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.3K
Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

1.2K
fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

21.0K
  • 可视化证实,模型专注于相关的大脑区域.

    • 结论:

    • 使用fNIRS数据的BWise-DA方法有效地提高了使用fNIRS数据的认知工作负载预测模型的概括性.
    • 基于MLPMixer的方法与对比学习相结合,为准确可靠的工作负载评估提供了一个有希望的方向.
    • 这些发现支持使用fNIRS进行现实世界的认知工作负载监控.