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

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</i>: 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). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods</i>: 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results</i>: 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion</i>: 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Significance</i>: This study significantly advances the applications of BCI and propels human-machine interaction towards a more direct realm.