Idle State Detection in SSVEP-Based Brain-Computer Interfaces
Idle State Detection in SSVEP-Based Brain-Computer Interfaces
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DOI：
10.1109/icbbe.2008.832
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发表时间：
2008-05
期刊：
影响因子：
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通讯作者：
Ran Ren;Guangyu Bin;Xiaorong Gao
中科院分区：
文献类型：
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作者：
Ran Ren;Guangyu Bin;Xiaorong Gao
近年来，脑-机接口在实验室的快速发展，为其在实际生活中的应用奠定了坚实的基础。其中，基于稳态视觉诱发电位(SSVEP)的脑机接口是一种很有前途的技术。它的稳定性和速度使其在不久的将来得到应用。为了实现它的实用性，需要制定一种可行的方法来检测空闲状态。本文提出了一种基于C0复杂度、主成分分析(PCA)和奇异谱分析(SSA)的方法。这种方法可称为主成分Co复杂性(PCC0)。结果表明，在SSVEP检测的平均准确率为80%的情况下，用该方法判断空闲状态的准确率为90%。这种方法可以进一步发展，用于在线的异步脑-机接口系统。
In recent years, the rapid development of Brain-Computer Interfaces in the laboratory has prepared a solid foundation for its application to real life situations. Among the techniques developed, the Steady-State Visual Evoked Potential (SSVEP)-based BCI is a promising one. Its stability and speed make it applicable in the near future. To realize its practicability, a workable method needs to be worked out to detect the idle state. In this paper, a method using C0 complexity, Principal Component Analysis (PCA) and Singular Spectrum Analysis (SSA) is proposed. This method can be called Principal-Component Co Complexity (PCC0). The results show that the idle state can be determined using this method with 90% accuracy when SSVEP can be detected with an average accuracy of 80%. This approach can be further developed for use in online asynchronous BCI systems.