Background: The rapid serial visual presentation (RSVP) paradigm is a high-speed paradigm of brain–computer interface (BCI) applications. The target stimuli evoke event-related potential (ERP) activity of odd-ball effect, which can be used to detect the onsets of targets. Thus, the neural control can be produced by identifying the target stimulus. However, the ERPs in single trials vary in latency and length, which makes it difficult to accurately discriminate the targets against their neighbors, the near-non-targets. Thus, it reduces the efficiency of the BCI paradigm.

Methods: To overcome the difficulty of ERP detection against their neighbors, we proposed a simple but novel ternary classification method to train the classifiers. The new method not only distinguished the target against all other samples but also further separated the target, near-non-target, and other, far-non-target samples. To verify the efficiency of the new method, we performed the RSVP experiment. The natural scene pictures with or without pedestrians were used; the ones with pedestrians were used as targets. Magnetoencephalography (MEG) data of 10 subjects were acquired during presentation. The SVM and CNN in EEGNet architecture classifiers were used to detect the onsets of target.

Results: We obtained fairly high target detection scores using SVM and EEGNet classifiers based on MEG data. The proposed ternary classification method showed that the near-non-target samples can be discriminated from others, and the separation significantly increased the ERP detection scores in the EEGNet classifier. Moreover, the visualization of the new method suggested the different underling of SVM and EEGNet classifiers in ERP detection of the RSVP experiment.

Conclusion: In the RSVP experiment, the near-non-target samples contain separable ERP activity. The ERP detection scores can be increased using classifiers of the EEGNet model, by separating the non-target into near- and far-targets based on their delay against targets.

背景：快速串行视觉表示（RSVP）范例是脑机接口（BCI）应用程序的高速范例。目标刺激唤起奇异球效应的事件相关电位（ERP）活动，可用于检测目标的发作。因此，可以通过识别目标刺激来产生神经控制。但是，单次试验中的ERP的潜伏期和时长各不相同，这使得很难准确地将目标与他们的邻居（几乎是非目标）区分开。因此，它降低了BCI范式的效率。

方法：为了克服对邻居进行ERP检测的困难，我们提出了一种简单但新颖的三元分类方法来训练分类器。新方法不仅将目标与所有其他样本区分开，而且进一步分离了目标样本，近乎非目标样本和其他远非目标样本。为了验证新方法的效率，我们进行了RSVP实验。使用带有或不带有行人的自然场景图片；以行人为目标。在演示过程中获取了10名受试者的磁脑电图（MEG）数据。EEGNet体系结构分类器中的SVM和CNN用于检测目标的发作。

结果：我们使用基于MEG数据的SVM和EEGNet分类器获得了相当高的目标检测分数。提出的三元分类方法表明，可以将近乎非目标的样本与其他样本区分开，并且分离显着提高了EEGNet分类器中的ERP检测得分。此外，新方法的可视化表明在RSVP实验的ERP检测中，SVM和EEGNet分类器具有不同的基础。

结论：在RSVP实验中，几乎非目标的样品包含可分离的ERP活性。可以使用EEGNet模型的分类器来提高ERP检测分数，方法是根据非目标相对于目标的延迟将非目标分为近目标和远目标。