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相关实验视频

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基于联合分布和深度适应的实例-表示传输方法,用于EEG情感识别.

Lei Zhu1, Fei Yu2, Aiai Huang2

  • 1School of Automation, Hangzhou Dianzi University, Hangzhou, 310000, China. zhulei@hdu.edu.cn.

Medical & biological engineering & computing
|November 2, 2023
PubMed
概括

这项研究介绍了联合分布式实例代表传输 (JD-IRT) 以更准确的电脑电图 (EEG) 情感识别. 这种新的方法改善了跨主题和跨会话的表现,增强了人机交互.

关键词:
域名适应领域适应这是一个EEGEEGEEGEEGEEGEEGEEG.情绪识别 情绪识别转移学习转移学习

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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:

  • 基于脑电图 (EEG) 的情绪识别对于推进人机交互至关重要.
  • 实际应用受到主题和会话变化的阻碍.
  • 转移学习提供了一个解决方案,现有的方法专注于实例或表示转移.

研究的目的:

  • 提出一种新的情绪识别方法,联合分布式实例代表转移 (JD-IRT),以解决EEG变异性.
  • 提高基于EEG的情绪识别系统的准确性和适用性.

主要方法:

  • 开发了JD-IRT,包括联合分发深度调整 (JDDA) 和实例代表转移 (I-RT).
  • JDDA使用适应层和内核来解决边际和条件分布差异,以实现深域适应.
  • I-RT使用实例传输来优化源域数据选择,以改善表示传输.

主要成果:

  • 跨学科准确度达到了83.21% (SEED),52.12% (SEED-IV) 和60.17% (SEED-V) 的目标.
  • 实现了91.29% (SEED),59.02% (SEED-IV) 和65.91% (SEED-V) 的跨会话准确率.
  • 对基准数据集的现有代表性方法进行了显著改进.

结论:

  • 拟议的JD-IRT方法有效地提高了EEG情绪识别在不同主体和会话中的准确性.
  • 这一进步为更强大,更实用的人机交互系统带来了希望.
  • 该研究强调了在EEG分析中将深度域适应与跨域学习的实例转移相结合的有效性.