A brain-computer interface-based vehicle destination selection system using P300 and SSVEP signals

In this paper, we propose a novel driver-vehicle interface for individuals with severe neuromuscular disabilities to use intelligent vehicles by using P300 and steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) to select a destination and test its performance in the laboratory and real driving conditions. The proposed interface consists of two components: the selection component based on a P300 BCI and the confirmation component based on an SSVEP BCI. Furthermore, the accuracy and selection time models of the interface are built to help analyze the performance of the entire system. Experimental results from 16 participants collected in the laboratory and real driving scenarios show that the average accuracy of the system in the real driving conditions is about 99% with an average selection time of about 26 s. More importantly, the proposed system improves the accuracy of destination selection compared with a single P300 BCI-based selection system, particularly for those participants with relatively low level of accuracy in using the P300 BCI. This study not only provides individuals with severe motor disabilities with an interface to use intelligent vehicles and thus improve their mobility, but also facilitates the research on driver-vehicle interface, multimodal interaction, and intelligent vehicles. Furthermore, it opens an avenue on how cognitive neuroscience may be applied to intelligent vehicles.