A novel stimulation for multi-class SSVEP-based brain-computer interface using patterns of time-varying frequencies

Steady-state visual evoked potential (SSVEP) has become one of the most widely employed modalities in online brain computer interface (BCI) because of its high signal-to-noise ratio. However, due to the limitations of brain physiology and the refresh rate of the display devices, the available stimulation frequencies that evoke strong SSVEPs are generally limited for practical applications. In this paper, we introduce a novel stimulation method using patterns of time-varying frequencies that can increase the number of visual stimuli with a fixed number of stimulation frequencies for use in multi-class SSVEP-based BCI systems. We then propose a probabilistic framework and investigate three approaches to detect different patterns of time-varying frequencies. The results confirmed that our proposed stimulation is a promising method for multi-class SSVEP-based BCI tasks. Our pattern detection approaches improved the detection performance significantly by extracting higher quality discriminative information from the input signal.