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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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EEG工作负载估计和分类:一个系统的审查.

Jahid Hassan1, Md Shamim Reza2, Syed Udoy Ahmed3

  • 1Electrical and Electronic Engineering, Pabna University of Science and Technology, Kismotprotap Pur, Pabna, Pabna, 6600, BANGLADESH.

Journal of neural engineering
|August 16, 2024
PubMed
概括
此摘要是机器生成的。

本系统性审查强调了机器学习 (ML) 和深度学习 (DL) 如何分析电脑电图 (EEG) 数据以估计认知工作负载. 结果显示了常见的ML/DL方法和影响模型准确性的因素,为改善人机交互铺平了道路.

关键词:
深度学习 (DL) 是指深度学习.心理工作负荷 (MWL)电脑电图 (EEG) 是一个电脑电图.机器学习 (ML) 是一种机器学习.

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科学领域:

  • 神经科学和人工智能 人工智能
  • 人与计算机的交互
  • 认知科学 认知科学

背景情况:

  • 电脑电图 (EEG) 对于评估不同领域的认知工作量至关重要.
  • 机器学习 (ML) 和深度学习 (DL) 越来越多地用于构建基于EEG的准确工作负载模型.
  • 需要进行系统审查,以整合使用ML/DL对EEG工作负载估计的研究.

研究的目的:

  • 系统地审查和编译研究认知工作负载估计和分类使用EEG数据与ML和DL技术.
  • 在这个领域确定常见的ML/DL算法,研究设计和性能指标.

主要方法:

  • 在主要的科学数据库 (SpringerLink,ACM,IEEE,PubMed,Science Direct) 进行系统的文献搜索,截至2024年2月16日.
  • 根据Prisma指南,根据预定义的纳入标准选择的研究.
  • 数据提取集中在研究设计,参与者人口统计,EEG特征,ML/DL算法和绩效指标上.

主要成果:

  • 在最初确定的125篇论文中,有33篇被纳入最终分析.
  • 支持矢量机器 (SVM),卷积神经网络 (CNN) 和循环神经网络 (RNN) 是最常用的ML/DL技术.
  • 在EEG数据中采样频率更高通常与模型准确度的提高相关;SVM,CNN和混合网络显示出强度.

结论:

  • 在各种应用中,ML从EEG数据有效地估计了各种应用中的心理工作负载.
  • 未来的进步需要多式联运数据集成,标准化和现实世界的验证.
  • 解决伦理方面的考虑和探索新的EEG特性将增强ML/DL模型,以改善人机交互和性能评估.