Asynchronous control of unmanned aerial vehicles using a steady-state visual evoked potential-based brain computer interface - Meriño, Lenis; Nayak, Tapsya; Kolar, Prasanna; Hall, Garrett; Mao, Zijing; Pack, Daniel J.; Huang, Yufei (Brain computer interfaces (Abingdon, England), 2017)
The goal of this study is to design an asynchronous steady-state visual evoked potential (SSVEP) BCI system to enable control of an unmanned aerial vehicle (UAV) with multiple commands. An SSVEP-based BCI system with six different flickering frequencies was constructed to realize six actuation commands for UAV control. In addition, asynchronous control was achieved by including a detection of the 'idle' brain state using a novel likelihood ratio test and the hover command was implemented for the idle state. Offline recording was conducted to evaluate the detection accuracies and a game-like online experiment was also conducted to assess the online performance of the proposed system. Forty-two subjects participated in offline recordings to evaluate the detection accuracy of commands as well as detection of the 'idle' state. An average error rate of 15% was obtained for detecting the six commands, whereas an average error rate of 23.06% was obtained for differentiating commands from idle brain states. For the online test, 11 subjects were recruited and all except two subjects successfully demonstrated control of the UAV by maneuvering the drone using all six commands and hover to acquire targets. Given our system design with a higher number of commands and online task difficulty, an average ITR of 0.98 bits/min was obtained. The developed SSVEP-based drone control system can execute a lot more commands than an imaginary motion-based drone control system and the asynchronous design significantly improves navigation. In addition, the proposed system can achieve good detection performance and ITR without any training.