A High-DOF BCI Control Strategy Mapping Discrete Commands to Continuous Motion for a Drone

Because of the non-stationary nature of electroencephalogram (EEG) signals, traditional non-invasive brain-computer interfaces (BCIs) usually only produce discrete commands, limiting their ability to control external devices continuously. This study proposes a novel BCI control strategy mapping multiple discrete commands to continuous motion, enabling real-time manipulation of a drone in four degrees of freedom (DOF). Our strategy used the fast steady state visual evoked potential (SSVEP) encoding and decoding method to convert user intentions into the drone’s flight status in near real-time. Simultaneously, the drone’s live video was embedded into the SSVEP stimuli, providing users with a first-person perspective control experience. In drone control experiments, participants successfully maneuvered the drone through complex path-following tasks in simulated and physical scenarios. The mean flight trajectory bias ratio was measured as 0.81, with a mean flight smoothness of -3.31 (measured by spectral arc length) and mean Fitts’s throughput of 9.18 bits/min. Notably, the brain-to-hand ratio (BHR) for all metrics approached 1, indicating that our non-invasive control system achieved comparable performance to manual control systems. These results suggest the effectiveness of our proposed BCI control strategy that maps discrete commands to continuous motion and extends the capabilities of non-invasive BCIs in continuous control scenarios. This study significantly advances the applications of BCI and propels human-machine interaction towards a more direct realm. Note to Practitioners—This work is motivated by the real-time and continuous control challenges in using non-invasive brain-computer interfaces (BCIs). Non-invasive BCIs establish a direct communication pathway between the human brain and external devices. Due to their safety and convenience, they are considered a promising human-machine interaction method for future practical applications. However, a major bottleneck is that non-invasive BCIs that generate discrete commands are unable to meet the demands of continuous control for external devices. Additionally, the low accuracy and high latency in decoding commands from human intent further distance current brain-control systems from practical application. In this paper, we propose a BCI strategy that includes a continuous encoding and decoding method for EEG signals. This strategy reduces the latency of command output while maintaining both the size of the command set and decoding accuracy. Furthermore, it introduces a method to map the decoded commands to continuous movements of external devices, effectively bridging the existing gap. We conducted extensive simulations and real-world experiments, demonstrating that the proposed strategy allows users to achieve continuous, real-time, four-degree-of-freedom control of a quadcopter using a non-invasive BCI. The analysis of results shows that, in terms of control accuracy, continuity, and operational efficiency, brain control is comparable to manual control. In the future, we aim to enhance the external devices with artificial intelligence to enable collaborative control between humans and machines, thereby advancing the practical development of BCIs.