Capsule Network for ERP Detection in Brain-Computer Interface
Capsule Network for ERP Detection in Brain-Computer Interface
复制标题
脑机接口中 ERP 检测的胶囊网络
DOI：
10.1109/tnsre.2021.3070327
复制
发表时间：
2021-01-01
影响因子：
4.9
通讯作者：
Gu, Zhenghui
中科院分区：
文献类型：
--
作者：
Ma, Ronghua;Yu, Tianyou;Gu, Zhenghui
事件相关电位（ERP）是大脑对特定事件或刺激产生的生物电活动，反映了认知过程中大脑的电生理变化。ERP在认知神经科学中具有重要意义，并已应用于脑机接口（BCI）。然而，由于在头皮上采集到的ERP信号微弱，且与自发脑电图（EEG）信号混合，其时空特征复杂，准确检测ERP具有挑战性。与传统神经网络相比，胶囊网络（CapsNet）用向量输出的胶囊取代了标量输出的神经元，使得各种输入信息能在胶囊中得到很好的保存。在本研究中，我们期望利用CapsNet提取ERP具有判别性的时空特征，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，我们提出了ERP - CapsNet用于在BCI拼写器应用中进行ERP检测。在脑机接口竞赛数据集和Akimpech数据集上的实验结果表明，ERP - CapsNet比现有技术取得了更好的分类性能。我们还使用解码器研究了编码在胶囊中的ERP的属性。结果表明，ERP - CapsNet依靠P300和P100成分来检测ERP。因此，ERP - CapsNet不仅是一种出色的ERP检测方法，还为ERP检测机制提供了有用的见解。
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.