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解码非侵入性脑电图信号通过一个两个歧视者对抗网络.

Xuguang Liu1, Changyi Yu2, Ye Li1

  • 1Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China.

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概括
此摘要是机器生成的。

这项研究引入了一种新的双歧视域对抗神经网络 (TD-DANN),用于使用脑电图 (EEG) 信号准确解码情绪. 该方法增强了一般化和个性化的情感特征表示,提高了非侵入性的生物传感准确性.

关键词:
深度学习是一种深度学习.域对抗性神经网络的领域.电脑脑电图 (EEG) 是一种电脑电图.情感识别 情感识别 情感识别这是一种非侵入性的非侵入性治疗方法.

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科学领域:

  • 神经科学和人工智能 人工智能
  • 生物信号处理和机器学习

背景情况:

  • 电脑电图 (EEG) 记录大脑活动来解码情绪,但由于大脑的个体差异和复杂的通道相互关系,它面临着挑战.
  • 现有的非侵入性生物感知方法在情绪识别中难以准确,因为受试者之间的变化和复杂的EEG信号模式.

研究的目的:

  • 提出一个双歧视域对抗神经网络 (TD-DANN) 来增强EEG信号的情感解码.
  • 通过对抗性学习实现更普遍和个性化的情感特征表示,以提高非侵入性生物传感的准确性.

主要方法:

  • 利用图形卷积来从EEG信号中提取特征,将频道建模为图形节点,具有动态学习的相邻矩阵.
  • 实施了两种歧视者方法:一个域区分器用于通用特征,一个个别区分器用于个性化情感适应性.
  • 采用对抗式学习来最大限度地减少源域和目标域之间的特征分布差异,增强特征通用性和个体一致性.

主要成果:

  • 在SEED数据集上达到 (98.45 ± 2.38)%的高主体依赖识别准确度,在SEED-IV数据集上达到 (84.40 ± 8.70) %的高主体依赖识别准确度.
  • 在SEED数据集上显示了强大的独立于主体的识别准确度 (89.45 ± 5.87) %,在SEED-IV数据集上显示了 (77.13 ± 7.97) %.
  • 在不同的数据集中,TD-DANN方法在主体依赖和主体独立的场景中显著提高了情绪解码精度.

结论:

  • 拟议的TD-DANN有效地解决了基于EEG的情绪解码中个体差异和复杂的通道相互关系的挑战.
  • 该方法能够学习通用和个性化的特征,验证了其在非侵入性生物传感中准确和适应性情绪识别的有效性.
  • 实验结果证实了TD-DANN的卓越性能,突出了其在情感计算和脑计算机接口中的实际应用潜力.