Objective. Single-trial electroencephalography (EEG) classification is of great importance in the rapid serial visual presentation (RSVP) task. Convolutional neural networks (CNNs), as one of the mainstream deep learning methods, have been proven to be effective in extracting RSVP EEG features. However, most existing CNN models for EEG classification do not consider the phase-locked characteristic of event-related potential (ERP) components very well in the architecture design. Here, we propose a novel CNN model to make better use of the phase-locked characteristic to extract spatiotemporal features for single-trial RSVP EEG classification. Based on the phase-locked characteristic, the spatial distributions of the main ERP component in different periods can be learned separately. Approach. In this work, we propose a novel CNN model to achieve superior performance on single-trial RSVP EEG classification. We introduce the combination of the standard convolutional layer, the permute layer and the depthwise convolutional layer to separately operate the spatial convolution in different periods, which more fully utilizes the phase-locked characteristic of ERPs for classification. We compare our model with several traditional and deep-learning methods in the classification performance. Moreover, we use spatial topography and saliency map to visually analyze the ERP features extracted by our model. Main results. The results show that our model obtains better classification performance than those of reference methods. The spatial topographies of each subject exhibit the typical ERP spatial distribution in different time periods. And the saliency map of each subject illustrates the discriminant electrodes and the meaningful temporal features. Significance. Our model is designed with better consideration of the phase-locked ERP characteristic and reaches excellent performance on single-trial RSVP EEG classification.

目标。单次试验脑电图 (EEG) 分类在快速串行视觉呈现 (RSVP) 任务中非常重要。卷积神经网络（CNN）作为主流的深度学习方法之一，已被证明在提取 RSVP EEG 特征方面是有效的。然而，大多数现有的用于 EEG 分类的 CNN 模型在架构设计中并没有很好地考虑事件相关电位 (ERP) 组件的锁相特性。在这里，我们提出了一种新颖的 CNN 模型，以更好地利用锁相特性来提取单次 RSVP EEG 分类的时空特征。基于锁相特性，可以分别学习ERP主要成分在不同时期的空间分布。方法。在这项工作中，我们提出了一种新颖的 CNN 模型，以在单次试验 RSVP 脑电图分类上实现卓越的性能。我们引入了标准卷积层、置换层和深度卷积层的组合，分别对不同周期的空间卷积进行操作，更充分地利用了ERPs的锁相特性进行分类。我们将我们的模型与几种传统和深度学习方法的分类性能进行了比较。此外，我们使用空间地形和显着图来直观地分析我们模型提取的 ERP 特征。主要结果. 结果表明，我们的模型比参考方法获得了更好的分类性能。每个受试者的空间地形在不同时间段表现出典型的ERP空间分布。每个主题的显着图说明了判别电极和有意义的时间特征。意义。我们的模型在设计时更好地考虑了锁相 ERP 特性，并在单次 RSVP EEG 分类上达到了优异的性能。