A hybrid BCI combining SSVEP and EOG and its application for continuous wheelchair control

Brain-computer interfaces (BCIs) have emerged as one of the most promising techniques for individuals with motor disabilities to control external devices directly without peripheral pathways. While the discrete control strategy is widely adopted in BCI systems, the continuous control strategy provides intuitive and fluent control of robotic devices. However, the steady-state visual evoked potential (SSVEP)-based BCI is still far from practical applications of continuous control. One of the existing challenges is the long erroneous transition time, i.e., the duration from when the subject switches towards a new target to when the BCI system recognizes this new target. Therefore, this study proposed a hybrid BCI combining SSVEP and electrooculography (EOG). First, the prior probability distribution of the intended target was obtained by the SSVEP detection method. Different from directly picking the one with the maximum probability as the conventional SSVEP-based BCI did, the captured saccade event in the EOG signal served as an additional event to optimize this probability distribution, and the target was then extrapolated and outputted. An offline experiment simulating the target switching process and an online experiment of virtual wheelchair control were designed and conducted. The proposed hybrid BCI significantly outperformed the conventional SSVEP-based BCI in both the offline experiment (continuous ac-curacy:64.99 +/- 3.44 % vs. 55.76 +/- 3.67 %, transition time: 1.19 +/- 0.073 s vs. 1.65 +/- 0.08 s) and the online wheelchair control experiment (success rate: 81.67 +/- 5.75 % vs. 16.67 %+/- 3.76 %; collision times with the dynamic obstacle: 1.50 +/- 0.50 vs. 8.25 +/- 0.37; minimum distance to dynamic obstacle: 1.41 +/- 0.13 m vs. 0.31 +/- 0.08 m). The results revealed that by combining the SSVEP and EOG signals, the proposed hybrid BCI can significantly diminish the transition time in SSVEP-based BCI and achieve continuous, fluent control. The present study provides a novel framework for integrating the SSVEP and EOG, and promotes the potential applications of continuous brain-actuated robotic devices.

Automated summary

Brain-computer interfaces (BCIs) have emerged as a promising technique for individuals with motor disabilities to control external devices. However, one of the challenges is the long transition time when switching to a new target. This study proposed a hybrid BCI that combines steady-state visual evoked potential (SSVEP) and electrooculography (EOG), which significantly reduces the transition time and achieves continuous, fluent control.