Idle State Detection in SSVEP-Based Brain-Computer Interfaces

Abstract

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.