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用于估计单轨引发潜力的域特定处理阶段 提高了CNN在检测错误潜力的性能.

Andrea Farabbi1, Luca Mainardi1

  • 1Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
概括

本研究引入了一种新的架构,用于检测误差潜力 (ErrP) 信号,首先通过增强电脑图 (EEG) 信号,然后使用卷积神经网络 (CNN). 与传统方法相比,这种方法显著提高了ErrP检测的准确性.

关键词:
大脑 计算机接口错误潜力是一个潜在的错误.一次性试验分析深度学习是一种深度学习.电脑脑电图 (EEG) 是一种电脑电图.机器学习是机器学习.信号处理 信号处理 信号处理

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科学领域:

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

背景情况:

  • 常规卷积神经网络 (CNN) 对于错误潜力 (ErrP) 检测过程原始电脑图 (EEG) 信号,包括背景噪声,这可能会降低准确性.
  • 现有的方法很难有效地将ErrP信号与背景EEG活动隔离开来,这阻碍了对ErrP存在的准确预测.

研究的目的:

  • 开发和评估一种用于增强单试 (ST) ErrP检测的新型架构.
  • 通过将信号增强与分类阶段分开来提高ErrP信号的预测准确度.

主要方法:

  • 实现了两阶段的架构:最初的ST ErrP增强,随后是基于CNN的分类.
  • 研究了各种ST ErrP估计技术,包括子空间规范化,连续波形变形和ARX模型.
  • 评估了不同的CNN分类器,如EEGNet,标准CNN和罗神经网络.

主要成果:

  • 拟议的架构显著优于CNN直接应用于原始EEG信号的性能.
  • 亚空间规范化方法在分类指标中表现出最实质性的改进.
  • 使用增强架构实现了高达14%的平衡精度增加和13.4%的F1得分增加.

结论:

  • 新的两级架构有效地通过在分类之前预处理EEG信号来增强ErrP信号检测.
  • 将ST ErrP增强技术,特别是次空间规范化与CNN相结合,为ErrP检测任务提供了一种优越的方法.
  • 这种方法为识别复杂EEG数据中的ErrP信号提供了更准确,更可靠的方法.