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.

脑电图（EEG）信号分类是运动图像脑机接口（BCI）系统中的关键部分。传统的监督学习方法在脑电分类中表现良好。不幸的是，未标记的样品比标记的样品更容易收集。另外，最近的研究表明，它可能通过利用未经选择的未标记样本来降低半监督学习的性能。为了解决这些问题，本文提出了一种新型的带有转移学习的半监督广义学习系统（TSS-BLS）。首先，使用联合分布自适应算法获得未标记样本的伪标记。然后，通过包含标签信息和伪标签信息的改进的流形正则化框架来构造TSS-BLS。最后，在三个BCI竞争数据集和UCI储存库中的四个基准数据集上评估了拟议的TSS-BLS的有效性，并与7种最新算法进行了比较，这些算法包括ELM，SS-ELM，HELM，SVM，LapSVM，BLS和GSS-BLS。实验结果表明，TSS-BLS的性能平均优于BLS和GSS-BLS。由此表明，TSS-BLS对于EEG分类是安全且有效的。