本文的第一作者为硕士生邢介震，题目为“A CNN-based comparing network for the detection of steady-state visual evoked potential responses”，被Neurocomputing录用。

以下为文章摘要：
Brain-computer interfaces (BCIs) based on Steady-State Visual Evoked Potentials (SSVEPs) has been attracting much attention because of its high information transfer rate and little user training. However, most methods applied to decode SSVEPs are limited to CCA and some extended CCA-based methods. This study proposed a comparing network based on Convolutional Neural Network (CNN), which was used to learn the relationship between EEG signals and the templates corresponding to each stimulus frequency of SSVEPs. The effectiveness of the proposed method is validated by comparing it with the standard CCA and other state-of-the art methods for decoding SSVEPs (i.e., CNN and TRCA) on the actual SSVEP datasets collected from 23 subjects. The comparison results indicate that the CNN-based comparing network can significantly improve the classification accuracy. Furthermore, the comparing network with TRCA achieved the best performance among three methods based on comparing network with the averaged accuracy of 91.24% (data length: 2s) and 86.15% (data length: 1s). The study validated the efficiency of the proposed CNN-based comparing network in decoding SSVEPs. It suggests that the comparing network with TRCA is a promising methodology for target identification of SSVEPs and could further improve the performance of SSVEP-based BCI system.

中文摘要参考：
基于稳态视觉诱发电位(SSVEPs)的脑机接口(BCIs)因为其信息传输率高和几乎不需要训练等优点而备受关注。然而，大部分用于解码SSVEPs的方法局限于CCA和一些基于CCA的延伸方法。本研究提出了一种基于卷积神经网络(CNN)的对比网络，该网络用来学习EEG信号和对应于各个SSVEPs刺激频率的模板之间的关系。通过将本文提出的方法与标准CCA和其他应用于解码SSVEPs的前沿方法（即CNN和TRCA）在23位被试身上采集的实际SSVEP数据集上得到的结果进行比较，验证了该方法的有效性。比较的结果表明，基于CNN的对比网络可以显著提高分类准确率。此外，采用TRCA的对比网络在三种基于对比网络的方法中表现最好，其平均准确率分别为91.24%(数据长度:2s)和86.15%(数据长度:1s)。本研究验证了所提出的基于CNN的对比网络在解码SSVEPs上的有效性。结果表明，采用TRCA的对比网络是一种很有前途的SSVEP目标识别方法，可以进一步提高基于SSVEP的BCI系统的性能。