Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
J Neural Eng. 2013 Dec;10(6):066017. doi: 10.1088/1741-2560/10/6/066017. Epub 2013 Nov 27.
The prediction of brain-computer interface (BCI) performance is a significant topic in the BCI field. Some researches have demonstrated that resting-state data are promising candidates to achieve the goal. However, so far the relationships between the resting-state networks and the steady-state visual evoked potential (SSVEP)-based BCI have not been investigated. In this paper, we investigate the possible relationships between the SSVEP responses, the classification accuracy of five stimulus frequencies and the closed-eye resting-state network topology.
The resting-state functional connectivity networks of the corresponding five stimulus frequencies were created by coherence, and then three network topology measures--the mean functional connectivity, the clustering coefficient and the characteristic path length of each network--were calculated. In addition, canonical correlation analysis was used to perform frequency recognition with the SSVEP data.
Interestingly, we found that SSVEPs of each frequency were negatively correlated with the mean functional connectivity and clustering coefficient, but positively correlated with characteristic path length. Each of the averaged network topology measures across the frequencies showed the same relationship with the SSVEPs averaged across frequencies between the subjects. Furthermore, our results also demonstrated that the classification accuracy can be predicted by three averaged network measures and their combination can further improve the prediction performance.
These findings indicate that the SSVEP responses and performance are predictable using the information at the resting-state, which may be instructive in both SSVEP-aided cognition studies and SSVEP-based BCI applications.
脑-机接口（BCI）性能的预测是 BCI 领域的一个重要课题。一些研究表明，静息态数据是实现这一目标的有前途的候选者。然而，到目前为止，静息态网络与基于稳态视觉诱发电位（SSVEP）的 BCI 之间的关系尚未得到研究。在本文中，我们研究了 SSVEP 响应、五类刺激频率的分类准确率与闭眼静息态网络拓扑之间的可能关系。
通过相干性构建了对应五个刺激频率的静息态功能连接网络，然后计算了每个网络的三个网络拓扑度量——平均功能连接、聚类系数和特征路径长度。此外，还使用典型相关分析对 SSVEP 数据进行了频率识别。
有趣的是，我们发现每个频率的 SSVEP 与平均功能连接和聚类系数呈负相关，但与特征路径长度呈正相关。在受试者之间的 SSVEP 平均频率之间，每个平均网络拓扑度量与 SSVEP 之间存在相同的关系。此外，我们的结果还表明，分类准确率可以通过三个平均网络度量来预测，它们的组合可以进一步提高预测性能。
这些发现表明，SSVEP 响应和性能可以使用静息态信息进行预测，这可能对 SSVEP 辅助认知研究和基于 SSVEP 的 BCI 应用都有指导意义。
