Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism.

中文翻译：

---

用于人机界面中ERP检测的胶囊网络

事件相关电位（ERP）是响应特定事件或刺激而在大脑中发生的生物电活动，反映了认知过程中大脑的电生理变化。ERP在认知神经科学中很重要，已应用于脑机接口（BCI）。但是，由于头皮上收集的ERP信号微弱，并与自发性脑电图（EEG）信号混合，并且它们的时空特征复杂，因此准确的ERP检测具有挑战性。与传统的神经网络相比，胶囊网络（CapsNet）用向量输出胶囊代替标量输出神经元，从而可以将各种输入信息很好地保存在胶囊中。在这项研究中，我们希望利用CapsNet提取ERP的可识别时空特征并将其编码在胶囊中，以减少有价值信息的丢失，从而提高BCI的ERP检测性能。因此，我们建议使用ERP-CapsNet在BCI拼写器应用程序中执行ERP检测。在BCI竞赛数据集和Akimpech数据集上的实验结果表明，与最新技术相比，ERP-CapsNet具有更好的分类性能。我们还使用解码器来调查胶囊中编码的ERP的属性。结果表明，ERP-CapsNet依靠P300和P100组件来检测ERP。因此，ERP-CapsNet不仅是一种出色的ERP检测方法，而且还为ERP检测机制提供了有用的见解。