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

Abstract

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% (± 8.02), 53.2% (± 3.11), and 41.9% (± 6.09) 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.

Exploring the Intersection of Brain–Computer Interfaces and Quantum Sensing: A Review of Research Progress and Future Trends

“…Beyond biomedicine, BCIs are being explored for education, entertainment, smart homes, art applications, industry, transport, and other domains. [1,9,22] [ 38,39] Endogenous BCIs Endogenous BCIs paradigms such as MI, visual imagery (VI), and speech imagery (SI).…”

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

“…Exogenous ones use external stimulus to generate the desired neural activation; while endogenous ones can operate independently of any stimulus. For a real-life application of imagined speech decoding, the most appropriate between these two systems would be the endogenous BCI (Lee et al, 2021a ).…”

BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection

“…The development of classification systems of this type of signals may result in their practical use and, above all, independence from the analysis of these signals by trained persons [1][2][3][4][5][6][7][8][9]. Currently, the determination of the frequency of EEG waves follows the following order: alpha (8-12 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), delta (<4 Hz), gamma (>30 Hz), theta (4-7 Hz) and mu (8)(9)(10)(11)(12). The latter band is closely related to the activity and performance of the brain [10][11][12][13][14].…”

“…Thus, it makes it possible to significantly improve the quality of life of people with disabilities, for example by converting the image of movement or facial expressions into a control or executive signal that a computer or other microprocessor system sends to various types of devices. The listed applications are presented in detail in the articles [7,[15][16][17][18][19][20][21][22][23][24][25].…”

“…22 show exemplary classification results with the resulting control signals and detections of nervous tics. Only two exemplary results of the sequence of movement commands for each nervous tic are presented.…”