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Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
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SSTM-IS:基于实例选择的简化STM方法,用于实时EEG情绪识别.

Shuang Ran1, Wei Zhong2, Danting Duan1

  • 1Key Laboratory of Media Audio & Video, Ministry of Education, Communication University of China, Beijing, China.

Frontiers in human neuroscience
|June 16, 2023
PubMed
概括

这项研究引入了一种用于转移学习的新型实例选择策略,以实现使用脑电图 (EEG) 信号的实时情绪识别. 开发的算法在很短的计算时间内实现了高精度,促进了实际应用.

关键词:
电脑电流信号 电脑电流信号大脑-计算机接口接口实例的选择选择实例的选择实时的情绪识别和情绪识别.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:

  • 脑电图 (EEG) 信号提供了对大脑活动的非侵入性监测,这对于大脑与计算机接口 (BCI) 至关重要.
  • 通过EEG识别客观情绪是一个关键的研究领域,但现有的系统经常在线处理数据,限制实时应用.
  • 人类情绪的动态性质需要情感BCI中的实时处理能力.

研究的目的:

  • 解决在情感BCI中离线处理的局限性.
  • 开发一种使用EEG信号进行实时情绪识别的新算法.
  • 为了提高情绪识别模型的速度和准确性,为新受试者提供情绪识别模型.

主要方法:

  • 在转移学习中引入实例选择策略.
  • 关于简化风格转移映射算法的建议.
  • 从源域数据中选择信息实例,并简化超参数更新策略,以实现更快,更准确的模型训练.

主要成果:

  • 在SEED (86.78%),SEED-IV (82.55%) 和定制离线数据集 (77.68%) 上实现了高识别精度.
  • 演示了快速计算时间:SEED的7s,SEED-IV的4s和离线数据集的10s.
  • 成功开发和整合一个实时情绪识别系统,包括EEG获取,处理,识别和可视化.

结论:

  • 拟议的算法在最短的时间内准确地识别情绪,满足实时应用程序的要求.
  • 实例选择和简化的风格转移映射提高了模型效率和新主体的有效性.
  • 实验结果验证了该算法的实用实时情绪识别能力,使用EEG.