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基于SSVEP的大脑计算机接口的动态分解图卷积神经网络.

Shubin Zhang1, Dong An1, Jincun Liu1

  • 1National Innovation Center for Digital Fishery, Beijing, 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Beijing, 100083, China; Ministry of Agriculture and Rural Affairs, Beijing, 100083, China; Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, Beijing, 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China.

Neural networks : the official journal of the International Neural Network Society
|January 26, 2024
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种新的动态分解图卷积神经网络 (DDGCNN),用于脑计算机接口 (BCI). 这种先进的方法有效地处理电脑电图 (EEG) 信号,以改善SSVEP分类和BCI控制.

关键词:
大脑计算机接口 (BCI)动态分解图卷积神经网络 (DDGCNN)电脑电图 (EEG) 是一个电脑电图.稳定状态视觉唤起潜力 (SSVEP)

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科学领域:

  • 神经科学是一个神经科学.
  • 计算机科学 计算机科学
  • 信号处理 信号处理

背景情况:

  • 稳态视觉唤起潜力 (SSVEP) 范式在脑计算机接口 (BCI) 中很常见.
  • 处理多通道脑电图 (EEG) 数据存在挑战,因为其非欧几里德性质,需要考虑通道间拓的方法.
  • 现有的方法经常与过度平滑和梯度消失在图形卷积网络 (GCNs) 这样的问题作斗争.

研究的目的:

  • 引入一种新的动态分解图卷积神经网络 (DDGCNN),用于分类SSVEPEEG信号.
  • 解决EEG数据处理的挑战,包括非欧几里德特征和道间拓关系.
  • 改进基于SSVEP的BCI中的特征提取和自适应聚合.

主要方法:

  • 开发了DDGCNN,结合了层层的动态图表,以对抗GCN中的过度平滑.
  • 使用密集的连接机制来缓解梯度消失.
  • 集成图形动态融合以增强传统的线性转换,以改善特征提取和聚合.
  • 在端到端系统中直接处理SSVEP时域信号.

主要成果:

  • DDGCNN在学习和从EEG拓结构中提取特征方面表现出卓越的表现.
  • 拟议的DDGCNN在两个基准数据集上超过了最先进的 (SOTA) 算法.
  • 成功展示了DDGCNN实现的同步BCI机器人鱼控制.

结论:

  • DDGCNN代表了基于SSVEP的BCI在EEG信号处理方面的重大进步.
  • 端到端,直接处理SSVEP时域信号使得DDGCNN易于部署.
  • 该方法有效地处理了EEG数据的非欧几里德性质,并提高了分类准确性.