面向无人机群控制的脑机接口
Towards Brain-Computer Interfaces for Drone Swarm Control
摘要：
非侵入性脑机接口（BCI）解码大脑信号以理解用户意图。随着无人机控制需求的增加，基于BCI的无人机控制系统已经取得了最新进展。特别是，基于脑信号的无人机群体控制可以为军事服务或工业灾难等各种行业提供服务。本文提出了一个原型的脑群接口系统的各种情况下，使用视觉图像范例。我们设计了一个可以在无人机群体控制模拟器环境下获取大脑信号的实验环境。通过该系统，我们收集了四种不同的情况下的脑电图（EEG）信号。七名受试者参加了我们的实验，并使用基本的机器学习算法评估分类性能。总平均分类精度高于机会水平精度。因此，我们可以确认基于EEG信号的无人机群控制系统执行高级任务的可行性。
英文摘要：
Noninvasive brain-computer interface (BCI) decodes brain signals to understand user intention. Recent advances have been developed for the BCI-based drone control system as the demand for drone control increases. Especially, drone swarm control based on brain signals could provide various industries such as military service or industry disaster. This paper presents a prototype of a brain swarm interface system for a variety of scenarios using a visual imagery paradigm. We designed the experimental environment that could acquire brain signals under a drone swarm control simulator environment. Through the system, we collected the electroencephalogram (EEG) signals with respect to four different scenarios. Seven subjects participated in our experiment and evaluated classification performances using the basic machine learning algorithm. The grand average classification accuracy is higher than the chance level accuracy. Hence, we could confirm the feasibility of the drone swarm control system based on EEG signals for performing high-level tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
