# Design of an EEG-based Drone Swarm Control System using Endogenous BCI Paradigms

arXiv: https://arxiv.org/abs/2012.03507 · HTML: https://mlsanity.com/paper/2012.03507/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks](https://mlsanity.com/paper/2106.04026/) (2021)
- [Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination](https://mlsanity.com/paper/2411.05811/) (2024)
- [DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder](https://mlsanity.com/paper/2107.07064/) (2021)
- [Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks](https://mlsanity.com/paper/2107.07335/) (2021)
- [Decoding EEG-based Workload Levels Using Spatio-temporal Features Under Flight Environment](https://mlsanity.com/paper/2312.09423/) (2023)
- [Decoding Fatigue Levels of Pilots Using EEG Signals with Hybrid Deep Neural Networks](https://mlsanity.com/paper/2411.09707/) (2024)

## Abstract (arXiv)

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.