基于图形卷积网络的新频道选择模型用于运动图像
Wei Liang1, Jing Jin1,2, Ian Daly3
1Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China.
Cognitive neurodynamics
|October 3, 2023
概括
这项研究引入了一种新的图形卷积神经网络 (GCN) 方法,用于在脑电脑接口 (BCI) 中选择脑电图 (EEG) 通道. 基于GCN的通道选择 (GCN-CS) 有效地识别了相关的EEG通道,提高了BCI的性能.
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
- 生物医学工程 生物医学工程
背景情况:
- 多通道脑电图 (EEG) 对于基于运动图像 (MI) 的脑计算机接口 (BCI) 至关重要.
- 冗余的EEG通道可能会阻碍BCI分类的性能.
- 有效的通道选择对于优化BCI系统至关重要.
研究的目的:
- 为BCI应用开发一种用于识别和选择相关EEG通道的新方法.
- 通过减少道冗余来提高BCI分类准确性.
- 为了利用图形卷积神经网络 (GCNs) 进行EEG通道选择.
主要方法:
- 提出了一个用于通道选择的图形卷积神经网络 (GCN) 模型,称为基于GCN的通道选择 (GCN-CS).
- 处理EEG通道作为图表中的节点,并将通道选择作为节点分类问题.
- 在三个不同的运动图像 (MI) 数据集上评估了GCN-CS方法.
主要成果:
- 在所有三个MI数据集中,GCN-CS方法显示了显著的性能改进.
- 实现的分类准确率为79.76% (数据集1),89.14% (数据集2) 和87.96% (数据集3).
- 拟议的方法有效地减少了道的数量,同时提高了BCI的性能,而不是竞争的方法.
结论:
- 在BCI中选择相关的EEG通道,GCN-CS是一种有效的方法.
- 该方法在机动图像任务的分类准确度上提供了显著的改进.
- 这种基于GCN的策略为优化脑计算机接口设计提供了一个有希望的方向.
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