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Semi-supervised feature extraction for EEG classification

机译：脑电分类的半监督特征提取

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摘要

Two semi-supervised feature extraction methods are proposed for electroencephalogram (EEG) classification. They aim to alleviate two important limitations in brain–computer interfaces (BCIs). One is on the requirement of small training sets owing to the need of short calibration sessions. The second is the time-varying property of signals, e.g., EEG signals recorded in the training and test sessions often exhibit different discriminant features. These limitations are common in current practical applications of BCI systems and often degrade the performance of traditional feature extraction algorithms. In this paper, we propose two strategies to obtain semi-supervised feature extractors by improving a previous feature extraction method extreme energy ratio (EER). The two methods are termed semi-supervised temporally smooth EER and semi-supervised importance weighted EER, respectively. The former constructs a regularization term on the preservation of the temporal manifold of test samples and adds this as a constraint to the learning of spatial filters. The latter defines two kinds of weights by exploiting the distribution information of test samples and assigns the weights to training data points and trials to improve the estimation of covariance matrices. Both of these two methods regularize the spatial filters to make them more robust and adaptive to the test sessions. Experimental results on data sets from nine subjects with comparisons to the previous EER demonstrate their better capability for classification.
机译：针对脑电图（EEG）分类，提出了两种半监督特征提取方法。他们旨在减轻脑机接口（BCI）中的两个重要限制。一种是由于需要简短的校准课程而需要小型训练集。第二个是信号的时变特性，例如训练和测试中记录的EEG信号通常表现出不同的判别特征。这些限制在BCI系统的当前实际应用中很常见，并且通常会降低传统特征提取算法的性能。在本文中，我们提出了两种通过改进以前的特征提取方法极能量比（EER）来获得半监督特征提取器的策略。两种方法分别称为半监督时间平滑EER和半监督重要性加权EER。前者构造了一个关于保存测试样本的时间流形的正则化术语，并将其作为对空间滤波器学习的一种约束。后者通过利用测试样本的分布信息来定义两种权重，并将权重分配给训练数据点和试验以改善协方差矩阵的估计。这两种方法都对空间过滤器进行了正则化处理，以使它们更加健壮并适应测试会话。来自九个受试者的数据集的实验结果与以前的EER进行了比较，证明了其更好的分类能力。

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