基于集成式张量域自适应的运动想象脑电分类

MI-EEG Classification Based on Ensemble Tensor Domain Adaptation

实际应用中脑电信号一直面临采集成本高、用户间差异大等问题,制约着基于脑电信号的运动想象领域的发展。针对跨受试者运动想象脑电信号识别任务,本研究提出了一种基于集成式张量域自适应的迁移学习方法。首先采用改进的欧氏空间对齐方法对多维脑电数据进行协方差对齐,消除原始数据的边缘分布偏移;其次提出基于张量子空间的改进联合概率分布方法,求得不同类别的映射子空间并实现未知标签的目标域识别分类。分别在7人200个样本和9人144个样本的BCI数据集上进行了实验,平均准确率达到82.18%和76.45%,证明了该方法在跨域分类识别上具有很好的性能。同时对于该方法各环节的效果也进行了可视化验证,展示了该集成式方法在跨域问题上的效果。

In clinical applications,EEG signals have been facing problems including high acquisition cost and large differences between users,which restrict the development of motor imaging based on EEG signals.Aiming at the task of cross-subject MI-EEG recognition,a transfer learning method based on ensemble tensor domain adaptation was proposed in this paper.Firstly,the improved Euclidean alignment method was used to co-align the multidimensional EEG data to eliminate the edge distribution shift of the original data.Secondly,an improved joint distribution adaptation method based on tensor subspace was proposed,which obtained different classes of mapping subspaces and performed label prediction of target domain samples.In this paper,experiments were carried out on BCI datasets of 200 samples for 7 people and 144 samples for 9 people,which proved that the proposed method had good performance in cross-domain classification recognition with average accuracy 82.18%and 76.45%.The effect of each part of the method was also visually verified,which showed the effectiveness of the ensemble method on cross-domain problems.