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Electroencephalography (EEG) signal classification is a crucial part in motor imagery brain–computer interface (BCI) system. Traditional supervised learning methods have performed well pleasing in EEG classification. Unfortunately, the unlabeled samples are easier to collect than labeled samples. In addition, recent studies have shown that it may degenerate performance of semi-supervised learning by exploiting unlabeled samples without selection. To address these issues, a novel semi-supervised broad learning system with transfer learning (TSS-BLS) is proposed in this paper. First, the pseudo-labels of unlabeled samples are obtained using the joint distribution adaptation algorithm. TSS-BLS is then constructed by an improved manifold regularization framework containing both labeled and pseudo-label information. Finally, the effectiveness of the proposed TSS-BLS is evaluated on three BCI competition datasets and four benchmark datasets from UCI repository and compared with seven state-of-the-art algorithms, including ELM, SS-ELM, HELM, SVM, LapSVM, BLS and GSS-BLS. Experimental results show that the performance of TSS-BLS is superior to BLS and GSS-BLS on average. It is thereby shown that TSS-BLS is safe and efficient for EEG classification. [ABSTRACT FROM AUTHOR]

In the past decades, regularized least squares classification (RLSC) is a commonly used supervised classification method in the machine learning filed because it can be easily resolved through the simple matrix analysis and achieve a close-form solution. Recently, some studies conjecture that the margin distribution is more crucial to the generalization performance. Moreover, from the view of margin distribution, RLSC only considers the first-order statistics (i.e., margin mean) and does not consider the actual higher-order statistics of margin distribution. In this paper, we propose a novel RLSC which takes into account the actual second-order (i.e., variance) information of margin distribution. It is intuitively expected that small margin variance will improve the generalization performance of RLSC from a geometric view. We incorporate the margin variance into the objective function of RLSC and achieve the optimal classifier by minimizing the margin variance. To evaluate the performance of our algorithm, we conduct a series of experiments on several benchmark datasets in comparison with RLSC, kernel minimum squared error, support vector machine and large margin distribution machine. And the empirical results verify the effectiveness of our algorithm and indicate that the margin distribution is helpful to improve the classification performance. [ABSTRACT FROM AUTHOR]