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使用深度学习方法在EEG信号中的情绪识别:一篇综述

Mahboobeh Jafari1, Afshin Shoeibi1, Marjane Khodatars1

  • 1Data Science and Computational Intelligence Institute, University of Granada, Spain.

Computers in biology and medicine
|September 14, 2023
PubMed
概括

深度学习 (DL) 显示了从脑电图 (EEG) 信号中改善情绪识别的前景,克服了信号变化和个人差异等挑战,以实现更客观的情绪检测.

关键词:
人工智能的人工智能是人工智能.生物信号 生物信号深度学习是一种深度学习.这是一个EEGEEGEEGEEGEEGEEGEEG.情绪识别 情绪识别

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科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 信号处理 信号处理

背景情况:

  • 情绪显著影响人类的认知和互动.
  • 生理信号,特别是脑电图 (EEG),为情绪检测提供了客观的措施.
  • 脑电图与中枢神经系统的直接联系和高空间分辨率使其对情绪识别研究具有价值.

研究的目的:

  • 检查深度学习 (DL) 技术的应用,用于使用EEG信号识别情绪.
  • 讨论与基于EEG的情绪识别相关的挑战.
  • 突出DL在应对这些挑战方面的潜力,并建议未来的研究方向.

主要方法:

  • 对用于EEG情绪识别的DL技术现有文献的审查和分析.
  • 探索EEG信号处理和特征提取用于情绪检测的挑战.
  • 讨论人工智能 (AI) 方法,包括机器学习 (ML) 和DL,用于处理复杂的EEG数据.

主要成果:

  • 基于EEG的情绪识别面临诸如信号变化,个体差异和缺乏标准化处理等挑战.
  • 由于其处理复杂和多样化的EEG数据的能力,DL技术显示出克服这些挑战的潜力.
  • 需要进一步的研究来确定最佳特征,并开发更强大的人工智能模型用于EEG情绪识别.

结论:

  • DL提供了一条有前途的途径,用于从EEG信号中推进客观情绪识别.
  • 解决信号变化和个体差异对于可靠的基于EEG的情绪检测至关重要.
  • 未来的研究应该专注于开发先进的DL模型和标准化处理技术,以加强情感识别.