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相关实验视频

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多源选择性图域适应网络用于跨主体EEG情绪识别.

Jing Wang1, Xiaojun Ning1, Wei Xu1

  • 1Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.

Neural networks : the official journal of the International Neural Network Society
|September 29, 2024
PubMed
概括

这项研究引入了一种用于脑电图 (EEG) 情绪识别的新型网络,通过利用多个主体数据来提高准确性. 该方法有效地提取主体不变的特征,以实现更强大的情绪检测.

关键词:
域名适应领域适应域名选择 域名选择电脑电图 (EEG) 是一个电脑电图.情绪识别 情绪识别图表神经网络的神经网络

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Cortical Source Analysis of High-Density EEG Recordings in Children
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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:

  • 情感大脑-计算机接口 (BCI) 对于情感的人与计算机的互动至关重要.
  • 电脑电图 (EEG) 数据中的个体差异对情绪识别构成重大挑战.

研究的目的:

  • 开发一种强大的EEG情绪识别方法,可以克服特定对象的变化.
  • 加强利用多主体数据,以改善目标主体情绪识别.

主要方法:

  • 提出了一个多源选择性图域适应网络 (MSGDAN).
  • 通过区分公共信息和个人信息,MSGDAN提取了对象不变表示.
  • 采用动态图形网络和图形域适应来捕捉并确保大脑功能连接和区域状态的不变性.

主要成果:

  • 在跨主体情绪识别实验中取得了优异的分类性能.
  • 在SEED,SEED-IV和DEAP数据集上得到验证,显示出强大的性能.

结论:

  • 该MSGDAN有效地解决了由个人差异引起的EEG情绪识别方面的挑战.
  • 拟议的网络增强了多源主体数据的融合,以更准确地检测情绪.