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基于多源选择的动态域自适应性EEG情绪识别.

Zhongmin Wang1,2,3, Mengxuan Zhao1

  • 1School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China.

The Review of scientific instruments
|January 8, 2025
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概括
此摘要是机器生成的。

这项研究引入了一种用于脑电图 (EEG) 情绪识别的新型动态域适应方法. 这种方法有效地解决了个体变异,提高了跨主体情绪识别准确度.

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科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 使用脑电图 (EEG) 识别情绪是一个重要的研究领域.
  • 脑电图信号的个体变化对跨主体情绪识别构成挑战.
  • 由于领域差异,现有的方法难以在不同的科目中进行概括.

研究的目的:

  • 提出使用多源选择的动态域适应性EEG情绪识别方法.
  • 通过考虑全球和本地子域差异来缓解域间差异.
  • 为了提高跨主体情绪识别的准确性和稳定性.

主要方法:

  • 一个多源选择策略可以过相关的主题领域.
  • 从源域和目标域提取常见的和域特定的特征.
  • 动态域名适应在培训期间调整重点从全球到本地分布差异.

主要成果:

  • 在SEED和SEED-IV数据集上实现了89.76%和65.28%的跨主题准确性.
  • 在SEED和SEED-IV数据集上实现了91.63%和67.83%的跨会话准确性.
  • 在跨学科场景中,与现有方法相比,显著改进.

结论:

  • 提出的动态域适应方法有效地解决了EEG信号的个体变化.
  • 该方法在跨主体和跨会话情绪识别任务中表现出高效.
  • 这项工作推进了基于EEG的可靠情绪识别领域.