论文标题
使用内源性BCI范式的基于EEG的无人机群控制系统的设计
Design of an EEG-based Drone Swarm Control System using Endogenous BCI Paradigms
论文作者
论文摘要
已经开发了非侵入性的脑部计算机界面（BCI），用于通过使用脑电图（EEG）信号来理解用户的意图。随着人工智能的最新发展，无人机控制系统有许多发展。可以反映用户意图的BCI特征导致基于BCI的无人机控制系统。当使用无人机群时，我们可以比使用单个无人机具有更多的优势，例如任务多样性。特别是，基于BCI的无人机群控制可能会为诸如兵役或行业灾难等各个行业提供许多优势。 BCI范式由外源和内源性范式组成。内源性范式可以独立于用户的意图与用户的意图一起运行。在这项研究中，我们设计了专门用于无人机群控制的内源性范例（即，运动图像（MI），视觉图像（VI）和语音图像（SI）），以及基于EEG的基于EEG的各种任务分类与无人机群有关。五个受试者参与了实验，并使用基本的机器学习算法评估了性能。 MI，VI和SI分别为51.1％，53.2％和41.9％。因此，我们证实了使用各种内源性范式增加无人机群控制自由度的可行性。
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.
