运动想象脑电图的空域特征迁移核学习方法

Transfer kernel learning method based on spatial features for motor imagery EEG

运动想象脑电(MI-EEG)信号在构建临床辅助康复的无创脑机接口(BCI)中获得了广泛关注。受限于不同被试者的MI-EEG信号样本分布存在差异,跨被试MI-EEG信号的特征学习成为研究重点。然而,现有的相关方法存在域不变特征表达能力弱、时间复杂度较高等问题,无法直接应用于在线BCI。为解决该问题,提出黎曼切空间特征迁移核学习(TKRTS)方法,并基于此构建了高效的跨被试MI-EEG信号分类算法。TKRTS方法首先将MI-EEG信号协方差矩阵投影至黎曼空间,并在黎曼空间上对齐不同被试者的协方差矩阵,同时提取黎曼切空间(RTS)特征;随后,学习RTS特征集上的域不变核矩阵,从而获得完备的跨被试MI-EEG特征表达,并通过该矩阵训练核支持向量机(KSVM)进行分类。为验证TKRTS方法的可行性与有效性,在3个公开数据集上分别进行多源域-单目标域以及单源域-单目标域的实验,平均分类准确率分别提升了0.81个百分点和0.13个百分点。实验结果表明,与主流方法对比,TKRTS方法提升了平均分类准确率并保持相似的时间复杂度。此外,消融实验结果验证了TKRTS方法对跨被试特征表达的完备性和参数不敏感性,适合构建在线脑接机口。

Motor Imagery ElectroEncephaloGram(MI-EEG)signal has gained widespread attention in the construction of non-invasive Brain Computer Interfaces(BCIs)for clinical assisted rehabilitation.Limited by the differences in the distribution of MI-EEG signal samples from different subjects,cross-subject MI-EEG signal feature learning has become the focus of research.However,the existing related methods have problems such as weak domain-invariant feature expression capabilities and high time complexity,and cannot be directly applied to online BCIs.To address this issue,an efficient cross-subject MI-EEG signal classification algorithm,Transfer Kernel Riemannian Tangent Space(TKRTS),was proposed.Firstly,the MI-EEG signal covariance matrices were projected into the Riemannian space and the covariance matrices of different subjects were aligned in Riemannian space while extracting Riemannian Tangent Space(RTS)features.Subsequently,the domain-invariant kernel matrix on the tangent space feature set was learnt,thereby achieving a complete representation of cross-subject MI-EEG signal features.This matrix was then used to train a Kernel Support Vector Machine(KSVM)for classification.To validate the feasibility and effectiveness of TKRTS method,multi-source domain to single-target domain and single-source domain to single-target domain experiments were conducted on three public datasets,and the average classification accuracy is increased by 0.81 and 0.13 percentage points respectively.Experimental results demonstrate that compared to state-of-the-art methods,TKRTS method improves the average classification accuracy while maintaining similar time complexity.Furthermore,ablation experimental results confirm the completeness and parameter insensitivity of TKRTS method in cross-subject feature expression,making this method suitable for constructing online BCIs.