在运动图像EEG分类中整合空间,光谱和时间特征的多分支网络
Xiaoqin Lian1,2, Chunquan Liu1,2, Chao Gao1,2
1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 102488, China.
Brain sciences
|August 28, 2025
概括
这项研究引入了一种新型的多分支深度神经网络来解码运动图像 (MI) 电脑电图 (EEG) 信号,显著提高了脑电脑接口 (BCI) 的性能. 这种先进的模型有效地捕捉了复杂的空间,光谱和时间特征,提高了MI-EEG解码的准确性.
科学领域:
- 神经科学
- 生物医学工程
- 机器学习
背景情况:
- 精确解码运动图像 (MI) 脑电图 (EEG) 信号对于有效的脑电脑接口 (BCI) 系统至关重要.
- 从复杂的非线性EEG信号中提取空间,光谱和时间维度的区分特征是一个重大挑战.
- 提高MI-EEG解码性能取决于解决这些多维特征提取的复杂性.
研究的目的:
- 开发一个深度神经网络,能够共同建模MI-EEG信号中的空间,光谱和时间特征.
- 通过捕获复杂的多维信号特征来提高MI-EEG解码的分类性能.
- 通过先进的信号处理,提高脑与计算机接口 (BCI) 系统的实用性和精度.
主要方法:
- 提出了一个多分支深度神经网络,整合了四个互补的特征提取分支.
- 该网络处理3D功率光谱密度张量和2D时间域EEG信号以实现统一的多维建模.
- 使用梯度加权类激活映射 (Grad-CAM) 来可视化模型优先的空间和光谱特征,帮助解释性.
主要成果:
- 拟议的模型在EEGMMIDB数据集 (五类任务) 上达到86.34%的准确性和0.829卡帕系数.
- 在BCI竞争IV数据集2a (BCIIV2A) 中,该模型获得了83.43%的准确性和0.779卡帕系数 (四类任务).
- 在MI-EEG分类中表现优于现有的最先进方法.
结论:
- 开发的多分支深度神经网络有效地解码运动图像 (MI) EEG信号,性能优于当前的方法.
- 该模型能够捕捉多维特征,从而提高脑电脑接口 (BCI) 的性能.
- 通过突出关键的空间通道和频段,Grad-CAM可视化证实了该模型的神经生理解释性.
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