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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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机器学习方法对ERP头皮分布分类的比较研究.

Roya Salehzadeh1, Firat Soylu2, Nader Jalili1

  • 1Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, United States of America.

Biomedical physics & engineering express
|June 6, 2023
PubMed
概括

机器学习方法使用脑电图 (EEG) 数据准确识别了指数配置 (FNC) 中的数值信息. 支持向量机在分类这些大脑模式方面显示了最高的准确性.

关键词:
电脑电磁波信号 电脑电磁波信号与事件相关的潜力 (ERP)指数的数字配置指数的数字配置.机器学习是机器学习.数字认知 数字认知

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科学领域:

  • 神经科学是一个神经科学.
  • 认知科学 认知科学
  • 机器学习 机器学习

背景情况:

  • 机器学习 (ML) 提供了对电脑脑图像 (EEG) 等非侵入性脑信号的高级分析.
  • 机器学习方法克服了传统脑电图 (EEG) 分析的局限性,例如事件相关潜力 (ERP).
  • 指数配置 (FNCs) 对于通信和算术至关重要,具有已知的神经处理差异.

研究的目的:

  • 将ML分类方法应用于ERP头皮分布.
  • 调查ML在识别不同指数配置 (FNC) 的数值信息方面的性能.
  • 通过三个形式分析FNC:监控,计数和非正规计数.

主要方法:

  • 使用了38名参与者的公开32通道EEG数据集.
  • 预处理的EEG数据和分类的ERP头皮时间分布.
  • 应用了六种ML方法:SVM,LDA,Naïve Bayes,决策树,KNN和神经网络.
  • 对所有FNC (12类) 和特定类别的FNC (4类) 进行分类.

主要成果:

  • 在这两种条件下,支持矢量机 (SVM) 实现了最高的分类准确性.
  • K-Nearest Neighbor (KNN) 显示了第二高的准确性,用于将所有FNC一起分类.
  • 神经网络成功地检索了特定类别的FNC分类的数值信息.

结论:

  • 机器学习方法是分析ERP头皮分布的有效工具.
  • 这项研究表明了ML在FNC内识别数值信息方面的潜力.
  • 强调探索用于大脑信号分析的各种ML技术的重要性.