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一个增强的Coati优化算法用于全球优化和特征选择在EEG情感识别.

Essam H Houssein1, Asmaa Hammad1, Marwa M Emam1

  • 1Faculty of Computers and Information, Minia University, Minia, Egypt.

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概括
此摘要是机器生成的。

这项研究介绍了eCOA,这是一个增强的优化算法,用于选择脑电图 (EEG) 功能以识别情绪. 这种新方法提高了分类准确性,优于现有的方法,从大脑信号中识别情绪状态.

关键词:
科蒂优化算法 科蒂优化算法电脑电磁波信号 电脑电磁波信号情绪识别 情绪识别功能选择 功能选择超听证学是一种超听证学.多层感知神经网络多层感知神经网络

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科学领域:

  • 神经科学是一个神经科学.
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 使用脑电图 (EEG) 信号识别情绪对于医疗保健,教育和游戏领域的应用至关重要.
  • 一个主要的挑战是缺乏标准化的特征集,导致情绪分类效率低下.
  • 脑电图数据的高维度进一步使准确的情绪识别复杂化.

研究的目的:

  • 引入一个先进的优化算法,eCOA,用于选择最佳的EEG特征来识别情绪.
  • 为了解决Coati优化算法 (COA) 的局限性,例如局部最佳和不平衡的利用.
  • 为了提高从EEG信号情绪分类的效率和准确性.

主要方法:

  • 通过将Coati优化算法 (COA) 与RUNge Kutta优化器 (RUN) 集成,并结合规模因子 (SF) 和增强解决方案质量 (ESQ) 机制,开发了eCOA.
  • 使用CEC'22测试套件和两个EEG情绪识别数据集 (DEAP和DREAMER) 评估了eCOA.
  • 应用eCOA用于使用多层感知神经网络 (MLPNN) 进行二进制和多类情绪分类 (价值,兴奋,支配).

主要成果:

  • 与COA和其他七种元启发方法相比,eCOA表现出优越的搜索能力,收性和多样性.
  • eCOA有效地进行了特征选择,确定了最佳的EEG特征,以最大限度地提高情绪分类任务的性能.
  • 获得了高分类准确度:DEAP激发的85.17%和DREAMER激发的95.21%,显著优于现有方法.

结论:

  • 拟议的eCOA算法为情绪识别中的EEG特征选择提供了强大而高效的解决方案.
  • 与当前最先进的方法相比,eCOA显著提高了情绪分类的准确性.
  • 这一进步为更复杂,更可靠的脑计算机接口和情感感知系统带来了希望.