An online hybrid BCI combining SSVEP and EOG-based eye movements

Hybrid brain-computer interface (hBCI) refers to a system composed of a single-modality BCI and another system. In this paper, we propose an online hybrid BCI combining steady-state visual evoked potential (SSVEP) and eye movements to improve the performance of BCI systems. Twenty buttons corresponding to 20 characters are evenly distributed in the five regions of the GUI and flash at the same time to arouse SSVEP. At the end of the flash, the buttons in the four regions move in different directions, and the subject continues to stare at the target with eyes to generate the corresponding eye movements. The CCA method and FBCCA method were used to detect SSVEP, and the electrooculography (EOG) waveform was used to detect eye movements. Based on the EOG features, this paper proposes a decision-making method based on SSVEP and EOG, which can further improve the performance of the hybrid BCI system. Ten healthy students took part in our experiment, and the average accuracy and information transfer rate of the system were 94.75% and 108.63 bits/min, respectively.

Automated summary

This paper proposes an online hybrid BCI system that combines SSVEP and eye movements to improve the performance of BCI systems. The SSVEP is induced by flashing 20 buttons evenly distributed in the GUI, while eye movements are generated by the subject continuously staring at the target after the flash. The CCA method and FBCCA method are used to detect SSVEP, and the electrooculography (EOG) waveform is used to detect eye movements. A decision-making method based on SSVEP and EOG features is proposed to further enhance the performance of the hybrid BCI system. The system achieves an average accuracy of 94.75% and an information transfer rate of 108.63 bits/min.