Single-Channel SSVEP-Based BCI for Robotic Car Navigation in Real World Conditions

Brain computer interfaces (BCIs) that are focused on navigation applications have been developed for patients suffering from severe paralysis to offer a means of autonomy. In SSVEP-based BCIs, users focus their gaze on flickering targets, which correspond to specific commands. Besides the accuracy of the target identification, several additional aspects are important for the development of a practical and useful BCI, such as low cost, ease of use and robustness to everyday-life conditions. In a previous paper, we presented an SSVEP-based BCI for remote robotic car navigation offering live camera feedback. In this paper, we further improve our implementation by adding a fourth direction (backwards), while redeveloping the software in a free-license environment (Python) using a smaller and lighter robotic car, easier to maneuver in interior spaces. Additionally, we study the possibility of using a single channel EEG and test the performance of our system in an offline session, as well as in an online realistic navigation in a predefined remote route. A total of 14 participants achieved an average offline accuracy of 81%, an average offline ITR of 117.1 bits/min and an average online completion time ratio (BCI completion time against optimal button completion time) of 2.27. All of the participants managed to finish the route under realistic conditions which indicates that our system has the potential to be integrated in the everyday life of immobilized patients.