Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu 210096, China.
IEEE Trans Biomed Eng. 2012 Sep;59(9):2496-505. doi: 10.1109/TBME.2012.2205383. Epub 2012 Jun 20.
In the context of electroencephalogram (EEG)-based brain-computer interfaces (BCI), common spatial patterns (CSP) is widely used for spatially filtering multichannel EEG signals. CSP is a supervised learning technique depending on only labeled trials. Its generalization performance deteriorates due to overfitting occurred when the number of training trials is small. On the other hand, a large number of unlabeled trials are relatively easy to obtain. In this paper, we contribute a comprehensive learning scheme of CSP (cCSP) that learns on both labeled and unlabeled trials. cCSP regularizes the objective function of CSP by preserving the temporal relationship among samples of unlabeled trials in terms of linear representation. The intrinsically temporal structure is characterized by an l(1) graph. As a result, the temporal correlation information of unlabeled trials is incorporated into CSP, yielding enhanced generalization capacity. Interestingly, the regularizer of cCSP can be interpreted as minimizing a nontask related EEG component, which helps cCSP alleviate nonstationarities. Experiment results of single-trial EEG classification on publicly available EEG datasets confirm the effectiveness of the proposed method.
在基于脑电图（EEG）的脑机接口（BCI）中，常见空间模式（CSP）被广泛用于空间滤波多通道 EEG 信号。CSP 是一种监督学习技术，仅依赖于标记的试验。由于当训练试验的数量较小时，会发生过拟合，因此其泛化性能会恶化。另一方面，大量的未标记试验相对容易获得。在本文中，我们提出了一种综合的 CSP 学习方案（cCSP），它可以在标记和未标记的试验上进行学习。cCSP 通过在线性表示中保留未标记试验的样本之间的时间关系来正则化 CSP 的目标函数。内在的时间结构由 l(1) 图来描述。结果，将未标记试验的时间相关信息纳入 CSP 中，从而提高了泛化能力。有趣的是，cCSP 的正则化项可以解释为最小化与任务无关的 EEG 分量，这有助于 cCSP 减轻非平稳性。在公开的 EEG 数据集上进行的单次 EEG 分类实验结果证实了该方法的有效性。
