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对EEG-fNIRS多式特征进行神经生理学数据增强,该增强基于一个否定扩散概率模型.

Li Chen1, Zhong Yin2, Xuelin Gu3

  • 1College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, PR China; School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.

Computer methods and programs in biomedicine
|January 15, 2025
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概括

这项研究引入了一种新的数据增强框架,用于使用脑电图 (EEG) 和功能近红外光谱 (fNIRS) 的混合脑电脑接口 (BCI). 拟议的方法通过生成更多的培训数据来提高深度学习模型的性能,从而提高BCI的准确性.

关键词:
电脑电图, 功能性近乎功能性的红外光谱学,多式脑电脑接口,脱光扩散概率模型,数据增强.

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 机器学习 机器学习

背景情况:

  • 混合脑电脑接口 (BCI) 结合了脑电图 (EEG) 和功能近红外光谱 (fNIRS) 提供了优势,而不是单模系统.
  • 深度学习显著改善BCI性能,但受到有限的大脑信号数据的阻碍.

研究的目的:

  • 提出一个EEG-fNIRS数据增强框架 (EFDA-CDG),以提高混合BCI系统的性能.
  • 为解决BCI应用程序的深度学习中的数据稀缺问题.

主要方法:

  • 开发了一个EEG-fNIRS数据增强框架 (EFDA-CDG),集成无声扩散概率模型 (DDPM) 和高斯噪声加法.
  • 统一的EEG和fNIRS数据维度通过特征提取和空间映射插值.
  • 在分类模块中包含了EEG特征注意力和fNIRS地形注意力.

主要成果:

  • 在三个公共数据库和一个自主收集的数据库上验证了EFDA-CDG框架.
  • 实现了高准确率:82.02%的运动图像,91.93%的思维算法,90.54%的公共数据集的n-back任务.
  • 在自主收集的数据集上,对吸毒成歧视的准确性达到了97.82%.

结论:

  • 欧洲粮食开发署-CDG框架有效地增加了混合EEG-fNIRSBCI系统的数据.
  • 这种增强显著提高了BCI应用程序的性能和准确性.