摘要
运动想象(MI)作为脑机接口(BCI)的重要应用,是运动康复训练的重要支撑。由于脑电的电极分布并非天然的欧式空间,对运动想象进行准确分类具有很大的挑战。而且现有方法仅仅考虑了脑电信号(EEG)中某一维度或者某两维度的信息,无法全面捕获脑电信号在时、频、空三个维度存在的内在特征。同时,脑电信号各维度上的动态关联强度影响了分类的鲁棒性。针对上述问题,提出了一种新颖的融合注意力的多维特征图卷积网络(AMFGCN)。首先,根据电极节点分布的非欧空间特性设计出图结构,充分表示电极间的空间相关性。其次,提出时-空、频-空的双分支框架,同时表示脑电信号在时域、频域和空间域上的信息。最后,通过融合注意力机制、图卷积和时间/频谱卷积从图表示中学习脑电信号的空间表示、时间依赖性和频率依赖性,并自适应捕获各维度上的动态关联强度。在四个公开脑机接口数据集上进行了实验,结果表明AMFGCN模型提高了分类性能,优于其他现有的运动想象分类方法。
Motor imagery(MI), an important application of brain-computer interface(BCI), is critical for sports rehabilitation. Since the spatial position of electrodes is not Euclidean, accurate classification of MI is extremely challenging. Existing methods only consider the information of a single dimension or two dimensions in electroencephalogram(EEG) signals, and cannot fully capture the inherent features of EEG signals in three dimensions of temporal, spectral and spatial. Meanwhile, dynamic correlations of EEG in each dimension affect the robustness of classification. To solve above problems, a novel model, named attention-based multi-dimensional feature graph convolutional network(AMFGCN), is proposed. Firstly, to model non-Euclidean spatial positions of electrodes and fully show spatial correlation between electrodes, a dedicated graph structure is designed. Secondly, a dual-branch framework of time-space and frequency-space dimensions is proposed, simultaneously decoding EEG signals from temporal, spectral and spatial domains. Thirdly, via fusing attention mechanisms into graph convolution and temporal/spectral convolution, temporal, spectral and spatial correlations are then dynamically captured respectively. Experiments on four public BCI datasets illustrate superior performance of AMFGCN, which outperforms state-of-the-art competitive methods.
作者
李珍琦
王晶
贾子钰
林友芳
LI Zhenqi;WANG Jing;JIA Ziyu;LIN Youfang(School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China;Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing Jiaotong University,Beijing 100044,China)
出处
《计算机科学与探索》
CSCD
北大核心
2022年第9期2050-2060,共11页
Journal of Frontiers of Computer Science and Technology
基金
中央高校基本科研业务费专项资金(2021JBM007)
国家自然科学基金(61603029)。
关键词
运动想象(MI)
注意力机制
图卷积网络
多维特征
脑电信号(EEG)
motor imagery(MI)
attention mechanism
graph convolutional network
multi-dimensional feature
electroencephalogram(EEG)
