Design of an EEG-based Drone Swarm Control System using Endogenous BCI Paradigms
This research provides an incremental step towards BCI-based drone swarm control for military or industrial disaster applications.
This study designed and tested endogenous brain-computer interface (BCI) paradigms (motor imagery, visual imagery, and speech imagery) for controlling drone swarms. The grand-averaged classification accuracies were 51.1% for motor imagery, 53.2% for visual imagery, and 41.9% for speech imagery, demonstrating the feasibility of increasing control degrees of freedom.
Non-invasive brain-computer interface (BCI) has been developed for understanding users' intentions by using electroencephalogram (EEG) signals. With the recent development of artificial intelligence, there have been many developments in the drone control system. BCI characteristic that can reflect the users' intentions led to the BCI-based drone control system. When using drone swarm, we can have more advantages, such as mission diversity, than using a single drone. In particular, BCI-based drone swarm control could provide many advantages to various industries such as military service or industry disaster. BCI Paradigms consist of the exogenous and endogenous paradigms. The endogenous paradigms can operate with the users' intentions independently of any stimulus. In this study, we designed endogenous paradigms (i.e., motor imagery (MI), visual imagery (VI), and speech imagery (SI)) specialized in drone swarm control, and EEG-based various task classifications related to drone swarm control were conducted. Five subjects participated in the experiment and the performance was evaluated using the basic machine learning algorithm. The grand-averaged accuracies were 51.1%, 53.2%, and 41.9% in MI, VI, and SI, respectively. Hence, we confirmed the feasibility of increasing the degree of freedom for drone swarm control using various endogenous paradigms.