运动想象脑电信号的跨域特征学习方法

运动想象脑电信号采集成本高且个体差异大，跨个体域构建脑电信号模式识别模型属于典型的小样本跨域学习任务。针对该任务，提出了一种运动想象脑电信号的跨域特征学习方法。该方法首先选择最优度量方法对齐协方差并提取共同空间模式特征；其次，在该特征基础上采用领域自适应方法学习目标域的最优跨域特征。为验证所提方法的可行性与有效性，采用经典模型识别跨域特征，在两个公开的数据集上进行对比实验。实验结果表明，通过所提方法学习到的跨域特征，在运动想象模式识别中，明显优于现有方法学习到的特征。此外，还详细对比了跨域特征学习方法的各项参数设置、性能及效率。

Cross-domain feature learning method for motor imagery EEG signals

Motor imagery EEG signals requires a high cost for recording, and there is a large difference for individual''s signals. Cross-subject motor imagery EEG signals recognition task belongs to a typical cross-domain learning problem with a small samples set. To solve this problem, this paper proposed a cross-domain feature learning method for motor imagery EEG signals to improve the recognition performance. The proposed method first selected the optimal measurement to align the covariance of EEG signals, and then extracted common spatial patterns(CSP) from the aligned EEG trials. Second, based on the CSP features, it selected an optimal domain adaptation algorithm to learn the optimal cross-domain features for the target domain. To validate the feasibility and effectiveness of the learned cross-domain features, it adopted a classical model to recognize the learned cross-domain features, and the comparative experiments were conducted on two public datasets. Experimental results show that the learned cross-domain features are obviously better than state-of-the-arts methods in recognition performance. In addition, this paper also compared the parameters setting, performance and efficiency for the proposed method.