On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.
On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.
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DOI：
10.1109/tnsre.2022.3198041
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发表时间：
2022
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基于运动想象(MI)的脑机接口(BCI)是一种重要的BCI范式，需要强大的分类器。近年来，深度学习技术的发展引起了人们对深度学习用于分类的浓厚兴趣，并导致了多种模型的出现。在这些模型中找到性能最好的模型，将有利于设计更好的BCI系统和分类器。然而，通过原始出版物直接比较各种模型的性能是困难的，因为用于测试模型的数据集彼此不同，太小，甚至无法公开获得。在这项工作中，我们选择了最近提出的五种MI-EEG深度分类模型：EEGNet、Short&Deep ConvNet、MB3D和ParaAtt，并在两个大型的公开可用的数据库上进行了测试，这些数据库分别有42个和62个人类受试者。我们的结果表明，两个模型在一个数据集上的表现相似，而EEGNet在第二个数据集上的表现最好，使用我们评估的参数，训练成本相对较小。
Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm which requires powerful classifiers. Recent development of deep learning technology has prompted considerable interest in using deep learning for classification and resulted in multiple models. Finding the best performing models among them would be beneficial for designing better BCI systems and classifiers going forward. However, it is difficult to directly compare performance of various models through the original publications, since the datasets used to test the models are different from each other, too small, or even not publicly available. In this work, we selected five MI-EEG deep classification models proposed recently: EEGNet, Shallow & Deep ConvNet, MB3D and ParaAtt, and tested them on two large, publicly available, databases with 42 and 62 human subjects. Our results show that the models performed similarly on one dataset while EEGNet performed the best on the second with a relatively small training cost using the parameters that we evaluated.