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基于EEG的优化眼睛状态分类使用修改的BER元启发算法.

Ahmed M Elshewey1, Amel Ali Alhussan2, Doaa Sami Khafaga2

  • 1Department of Computer Science, Faculty of Computers and Information, Suez University, P.O.Box: 43221, Suez, Egypt. ahmed.elshewey@fci.suezuni.edu.eg.

Scientific reports
|October 18, 2024
PubMed
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此摘要是机器生成的。

修改的Al-Biruni地球半径 (MBER) 算法通过使用电脑图 (EEG) 数据提高了眼睛状态分类的准确性. 这种新的方法优化了机器学习模型,在区分开眼和闭眼状态时达到96.12%的准确性.

科学领域:

  • 生物医学工程 生物医学工程
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 从EEG信号准确分类眼睛状态对于各种应用至关重要.
  • 现有的算法在对二进制分类任务的精度和特征选择方面面临挑战.

研究的目的:

  • 引入和评估修改的Al-Biruni地球半径 (MBER) 算法,以改进眼睛状态分类.
  • 为了比较MBER的性能与已建立的优化算法,如BER,PSO,WAO,GWO和GA.

主要方法:

  • 脑电图数据预处理:缩放,规范化和零值消除.
  • 实施MBER算法用于二进制特征选择.
  • 使用多个机器学习分类器 (KNN,DT,RF等) 的评估. 使用KNN作为优化的健身功能.
  • 使用ANOVA和Wilcoxon签名等级测试进行统计分析.

主要成果:

  • 与其他优化器相比,MBER算法在单模基准函数上的性能优于其他优化器.
  • 通过MBER优化的K-最近邻居 (KNN) 模型实现了高性能指标:精度 (0.959),NPV (0.965),F-Score (0.963),准确性 (0.961),灵敏度 (0.971) 和特异性 (0.950).
  • 整体眼睛状态分类的准确性达到了96.12%.
关键词:
阿尔-比鲁尼 地球半径优化分类 分类 分类 分类.眼睛状态 眼睛状态功能选择 功能选择进行元启发式优化优化.

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结论:

  • 修改的Al-Biruni地球半径 (MBER) 算法显著提高了眼睛状态分类的准确性.
  • 优化MBER的KNN提供了一种强大而有效的方法来分析EEG数据以检测眼睛状态.
  • 拟议的算法对需要精确的眼睛状态监测的现实世界应用具有前景.