Regularized common spatial patterns with subject-to-subject transfer of EEG signals
Regularized common spatial patterns with subject-to-subject transfer of EEG signals
复制标题
正则化的常见空间模式与脑电图信号的主体间传输
DOI：
10.1007/s11571-016-9417-x
复制
发表时间：
2017-04-01
影响因子：
3.7
通讯作者：
Wang, Haixian
中科院分区：
文献类型：
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作者：
Cheng, Minmin;Lu, Zuhong;Wang, Haixian
在脑-机接口(BCI)系统的背景下，利用公共空间模式(CSP)方法提取判别空间滤波器，用于脑电信号的分类。然而，当从新的BCI用户收集少量训练样本时，CSP的分类性能通常会恶化。在本文中，我们提出了一种方法，只需少量的训练数据集就能保持或提高系统的识别精度。该方法通过使用迁移学习策略对经典CSP技术进行正则化而形成。具体地说，我们通过最小化主题间特征之间的差异，将涉及相同任务的主题间信息纳入CSP分析。在两个BCI竞赛数据集上的实验结果表明，该方法比传统的CSP方法有很大的提高分类性能；基于少量可用训练样本，变换后的变体几乎在所有情况下都被证明是成功的。
In the context of brain-computer interface (BCI) system, the common spatial patterns (CSP) method has been used to extract discriminative spatial filters for the classification of electroencephalogram (EEG) signals. However, the classification performance of CSP typically deteriorates when a few training samples are collected from a new BCI user. In this paper, we propose an approach that maintains or improves the recognition accuracy of the system with only a small size of training data set. The proposed approach is formulated by regularizing the classical CSP technique with the strategy of transfer learning. Specifically, we incorporate into the CSP analysis inter-subject information involving the same task, by minimizing the difference between the inter-subject features. Experimental results on two data sets from BCI competitions show that the proposed approach greatly improves the classification performance over that of the conventional CSP method; the transformed variant proved to be successful in almost every case, based on a small number of available training samples.