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MSLTE:多个自我监督的学习任务,以提高EEG情绪识别能力.

Guangqiang Li1, Ning Chen1, Yixiang Niu1

  • 1School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, People's Republic of China.

Journal of neural engineering
|April 8, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了自我监督的学习任务,以改善电脑电图 (EEG) 情感识别,增强模型概括性和减少过度拟合,以获得更可靠的情感分类.

关键词:
脑电图 (EEG) 情绪识别 情绪识别图形自编码器的自编码器基于面具的自我监督学习学习.多任务学习是多任务学习.在重量分配方面,重量共享是很重要的.

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科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 脑电图 (EEG) 采集设备可能不稳定,导致频道或频段的信息丢失.
  • 现有的EEG情感识别模型往往忽略了这种不稳定性,导致过度拟合和糟糕的概括.
  • 这需要强大的模型,能够处理杂或不完整的EEG数据.

研究的目的:

  • 为了提高概括性,并减少EEG情绪识别模型中的过度拟合.
  • 为了解决因设备不稳定而导致的EEG数据中的信息丢失.
  • 开发一种新型模型,包括自我监督学习,以改进EEG分析.

主要方法:

  • 引入了频道掩蔽和频率掩蔽,以模拟EEG信息丢失.
  • 开发了两项自主监督学习任务,使用掩盖图形自动编码器 (GAE) 来进行特征重建.
  • 实现了图形解码器之间的重量共享 (WS) 机制,以实现可靠的特征重建.
  • 采用了自适应式多任务减重 (AWML) 策略,结合了监督和自我监督的减重.

主要成果:

  • 拟议的模型在SEED,SEED-V和DEAP数据集中实现了更高的平均情绪分类准确性.
  • 模型中的每个模块都为性能提升做出了重大贡献.
  • 与最先进的模型相比,证明了提高训练效率,减少模型大小和更低的计算复杂性.
  • 对关键参数变化的敏感性较小.

结论:

  • 自主监督的学习任务有效地提高了EEG情感识别模型的概括性,并减轻了过度匹配.
  • 拟议的方法为基于EEG的情绪分类提供了一个更强大,更有效的方法.
  • 该模型的架构可以适应其他EEG分类任务.