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基于自我注意的卷积神经网络和时间频率的共同空间模式,用于增强运动图像分类.

Rui Zhang1, Guoyang Liu1, Yiming Wen1

  • 1School of Microelectronics, Shandong University, Jinan 250100, China.

Journal of neuroscience methods
|August 23, 2023
PubMed
概括

这项研究通过使用一种新的自我注意CNN和TFCSP方法,增强了大脑-计算机接口 (BCI) 的运动图像 (MI) 分类. 这种方法改善了EEG信号解码,用于神经康复应用.

关键词:
大脑与计算机的接口.电脑电图 (EEG) 是一个电脑电图.运动图像中的运动图像.基于自我注意的卷积神经网络.时间频率共同空间模式 (TFCSP)

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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:

  • 基于运动图像 (MI) 的脑计算机接口 (BCI) 显示了神经康复的潜力.
  • 解码MIEEG信号是具有挑战性的,因为个体大脑的变化,需要更好的分类性能.

研究的目的:

  • 提出基于自我注意的卷积神经网络 (CNN) 与时间频率共同空间模式 (TFCSP) 结合,用于增强MI分类.
  • 用MI EEG数据集的数据增强策略来处理有限的训练数据.

主要方法:

  • 基于自我注意力的CNN提取时间和空间EEG信息,注意力模块计算通道重量.
  • 时间频率共同空间模式 (TFCSP) 提取多尺度时间频率空间特征.
  • 来自TFCSP和自我注意力CNN的特征被连接为最终的MI分类.

主要成果:

  • 拟议的方法在BCI竞争IVIIa数据集上实现了79.28%的平均准确率.
  • 该方法在BCI竞争IIIIIIa数据集中平均准确率为86.39%.
  • 与最先进的方法相比,观察到更高的分类准确度.

结论:

  • 自我注意力CNN和TFCSP的组合有效地利用时间频率空间EEG信息.
  • 这种方法显著提高了MI分类的性能.
  • 拟议的方法为实际的神经康复应用提供了更高的准确性.