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基于EEG的运动图像分类的深域适应框架与相关性对齐.

Xiao-Cong Zhong1, Qisong Wang1, Dan Liu1

  • 1School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.

Computers in biology and medicine
|July 13, 2023
PubMed
概括
此摘要是机器生成的。

收集足够的脑电图 (EEG) 数据用于脑电脑接口是具有挑战性的. 我们的深度域调整框架与相关性对齐 (DDAF-CORAL) 提高了不同数据域的运动图像分类准确性.

关键词:
大脑与计算机接口 (BCI)相关性对齐对齐是相关性对齐.域名适应 (DA) 是指域名适应.电脑电图 (EEG) 是一种电脑电图.运动图像 (MI)

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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
  • 生物医学工程 生物医学工程

背景情况:

  • 大脑计算机接口 (BCI) 面临着电脑脑图 (EEG) 数据的挑战,原因是耗时的获取和注释.
  • 传统的分类方法在运动图像任务中扎,当EEG数据在不同受试者或时间段 (跨领域) 之间有所不同时.
  • 这种分布差异显著降低了BCI的分类准确性.

研究的目的:

  • 提出一个新的深域适应框架与相关性对齐 (DDAF-CORAL) 强大的运动图像分类.
  • 解决EEG数据在不同领域 (受试者,会议) 的分布差异问题.
  • 为了提高运动成像任务的BCI的准确性和可靠性.

主要方法:

  • 一个两阶段的深度框架 (DDAF-CORAL) 被开发出来,从原始EEG数据中提取深度特征.
  • 使用相关性对齐 (CORAL) 通过对齐特征分布共差来最大限度地减少分布分歧.
  • 同时优化分类和适应损失,以实现歧视性分类和低特征差异.

主要成果:

  • 在三个实验数据集中,DDAF-CORAL方法有效地减少了源和目标EEG数据之间的分布差异.
  • 在两类运动图像分类任务中表现出卓越的性能.
  • 实现了高准确率:92.9%在会话内,0.761卡帕跨会话,和83.3%跨主题.

结论:

  • 拟议的DDAF-CORAL框架在跨域BCI应用中显著提高了运动图像分类的准确性.
  • 这种方法为克服基于EEG的BCI中的数据变异性挑战提供了可行的解决方案.
  • 这些发现强调了深度域调整的潜力,以提高BCI性能并减少对广泛标记数据的依赖.