Idle State Detection in SSVEP-based Brain-Computer Interfaces
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 C0 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.
BCI SSVEP scalp EEG C0 complezity PCA SSA PCC0 idle state detection
Ran Ren Guangyu Bin Xiaorong Gao
Tsinghua University Beijing, P.R. China
国际会议
上海
英文
2012-2015
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）