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自动EER:基于EEG的自动情绪识别与神经架构搜索

Yixiao Wu1, Huan Liu1,2, Dalin Zhang3

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China.

Journal of neural engineering
|August 3, 2023
PubMed
概括

AutoEER自动化了深度学习模型的设计,用于脑电图 (EEG) 情绪识别. 这种框架显著提高了准确性,减少了手工劳动,推进了EEG分析.

关键词:
电脑电图 (EEG) 是一个电脑电图.情感识别 情感识别 情感识别神经架构搜索 (NAS) 是指神经架构搜索.搜索空间 空间 搜索空间

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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
  • 机器学习 机器学习

背景情况:

  • 使用脑电图 (EEG) 的情绪识别对于便携式设备的应用越来越重要.
  • 深度学习模型在EEG情绪识别方面表现出色,但需要耗时的手动设计和定制.
  • 神经架构搜索 (NAS) 为优化深度网络提供了自动化解决方案.

研究的目的:

  • 引入AutoEER,一个使用定制NAS的框架,用于基于EEG的情绪识别中的自动最佳网络结构发现.
  • 设计一个专门的搜索空间,捕捉时间和空间EEG属性.
  • 开发一种新的参数化策略,以获得最佳的网络结构.

主要方法:

  • 拟议的AutoEER框架利用NAS进行EEG情绪识别.
  • 开发了一个定制的搜索空间,将时间和空间EEG特征的运算符结合起来.
  • 实施了用于网络结构优化的新型参数化策略.

主要成果:

  • 在DEAP和SEED数据集上,AutoEER的性能优于最先进的手册和NAS模型.
  • 与WangNAS相比,实现了0.93%的平均准确性改进.
  • 与LiNAS相比,F1平均得分提高了4.51%.
  • 生成的架构显示出优越的可转移性.

结论:

  • AutoEER为基于EEG的情绪识别模型设计提供了一种新的,自动化的方法.
  • 专门的搜索空间和参数化策略产生了高性能,可转移的模型.
  • 在EEG研究中,AutoEER显著降低了手工劳动和时间成本,有望推动该领域的发展.