Zhang Chuncheng, Qiu Shuang, Wang Shengpei, He Huiguang
National Laboratory of Pattern Recognition and Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Front Comput Neurosci. 2021 Feb 26;15:619508. doi: 10.3389/fncom.2021.619508. eCollection 2021.
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. 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. 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. 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.
快速序列视觉呈现（RSVP）范式是脑机接口（BCI）应用的一种高速范式。目标刺激会诱发奇球效应的事件相关电位（ERP）活动，可用于检测目标的出现。因此，通过识别目标刺激可以实现神经控制。然而，单次试验中的ERP在潜伏期和时长上存在差异，这使得难以准确区分目标与其相邻的近非目标。因此，这降低了BCI范式的效率。为了克服针对相邻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。新方法不仅能区分目标与所有其他样本，还能进一步将目标、近非目标和其他远非目标样本区分开来。为了验证新方法的有效性，我们进行了RSVP实验。使用了有无行人的自然场景图片；有行人的图片用作目标。在呈现过程中采集了10名受试者的脑磁图（MEG）数据。使用EEGNet架构分类器中的支持向量机（SVM）和卷积神经网络（CNN）来检测目标的出现。基于MEG数据，我们使用SVM和EEGNet分类器获得了相当高的目标检测分数。所提出的三元分类方法表明，近非目标样本可以与其他样本区分开来，这种区分显著提高了EEGNet分类器中的ERP检测分数。此外，新方法的可视化显示了SVM和EEGNet分类器在RSVP实验的ERP检测中的不同基础。在RSVP实验中，近非目标样本包含可分离的ERP活动。通过基于EEGNet模型的分类器，根据非目标相对于目标的延迟将其分为近目标和远目标，可以提高ERP检测分数。
