Waveform-Based Multi-Stimulus Coding for Brain-Computer Interfaces Based on Steady-State Visual Evoked Potentials

Multiple stimulus coding plays an important role in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). In conventional SSVEP-based BCIs, multiple visual stimuli are modulated with different properties such as frequencies and/or phases. However, the number of properties that can be assigned to visual stimuli rendered on a computer monitor is always limited by its refresh rate, leading to a system with limited commands or functions. To alleviate this issue, this study proposes a novel waveform-based stimulus coding method that uses modulation waveforms to differentiate resulting SSVEPs. In this paper, the discriminability of 12-class SSVEPs modulated by three types of waveforms (i.e., rectangle, sinusoidal and triangle waveforms) and four frequencies (i.e., 12, 13, 14, and 15 Hz) was investigated by computing its classification accuracy. The results showed the SSVEPs modulated by different waveforms can be successfully distinguished when using the state-of-the-art canonical correlation analysis (CCA)-based method with an average accuracy of 92.31%. This result suggests that the proposed method has great potential to significantly increase the number of functions in an SSVEP-based BCI system.