Most of the Brain-Computer Interface (BCI) publications, which propose artificial neural networks for Motor Imagery (MI) Electroencephalography (EEG) signal classification, are presented using one of the BCI Competition datasets. However, these databases contain MI EEG data from less than or equal to 10 subjects . In addition, these algorithms usually include only bandpass filtering to reduce noise and increase signal quality. In this article, we compared 5 well-known neural networks (Shallow ConvNet, Deep ConvNet, EEGNet, EEGNet Fusion, MI-EEGNet) using open-access databases with many subjects next to the BCI Competition 4 2a dataset to acquire statistically significant results. We removed artifacts from the EEG using the FASTER algorithm as a signal processing step. Moreover, we investigated whether transfer learning can further improve the classification results on artifact filtered data. We aimed to rank the neural networks; therefore, next to the classification accuracy, we introduced two additional metrics: the accuracy improvement from chance level and the effect of transfer learning. The former can be used with different class-numbered databases, while the latter can highlight neural networks with sufficient generalization abilities. Our metrics showed that the researchers should not avoid Shallow ConvNet and Deep ConvNet because they can perform better than the later published ones from the EEGNet family.

翻译：大多数脑-计算机接口（BCI）出版物在提出用于运动想象（MI）脑电图（EEG）信号分类的人工神经网络时，均使用BCI竞赛数据集之一进行展示。然而，这些数据库包含的MI EEG数据来自不超过10个受试者。此外，这些算法通常仅包含带通滤波以减少噪声并提高信号质量。本文中，我们在BCI竞赛4 2a数据集之外，使用包含大量受试者的开放获取数据库，对5种著名神经网络（Shallow ConvNet、Deep ConvNet、EEGNet、EEGNet Fusion、MI-EEGNet）进行了比较，以获得统计显著的结果。我们采用FASTER算法作为信号处理步骤，从EEG中去除伪迹。此外，我们研究了迁移学习是否能够进一步改进基于伪迹过滤数据的分类结果。我们旨在对神经网络进行排序；因此，除分类准确率外，我们引入了两个额外指标：相对于随机水平的准确率提升和迁移学习的效果。前者可用于不同类别数的数据库，后者则可突出具有足够泛化能力的神经网络。我们的指标表明，研究者不应回避Shallow ConvNet和Deep ConvNet，因为它们的表现可能优于后续发表的EEGNet家族网络。