TY -非盟的邵Lei盟——张,龙钰盟——Belkacem Abdelkader Nasreddine盟——张翳明非盟-陈,小琪AU -李,霁盟——刘Hongli PY - 2020 DA - 2020/01/11 TI - EEG-Controlled Wall-Crawling清洁机器人使用SSVEP-Based脑机接口SP - 6968713六世- 2020 AB -辅助,自适应,基于脑电图的机器人控制与导航的康复应用在维度和范围上都正在发生重大转变。在人工智能的背景下，医疗和非医疗机器人得到了迅速的发展，并逐渐被用于提高人们的生活质量。我们致力于将大脑与移动家庭机器人连接起来，通过将大脑信号转换为计算机命令，构建一个脑-机接口，通过显著提高残疾人和健全人的自主性、机动性和能力，有望大大提高他们的生活质量。一些类型的机器人已经使用BCI系统来控制，以完成实时的简单和/或复杂的任务，具有高性能。本文设计了一种基于脑电图的移动爬壁清扫机器人智能遥操作系统。该机器人采用履带式，而不是传统的轮式，用于清洁窗户或地板。基于脑电信号的机器人爬墙和清洁机器人位置控制系统，从采集的脑电图信号中提取稳态视觉诱发电位(SSVEP)。本文提出的基于ssvep的BCI视觉刺激界面由4个不同频率(6hz、7.5 Hz、8.57 Hz和10hz)的闪烁片组成。7名受试者通过大脑活动观察相应的闪烁，顺利控制了清洁机器人的运动方向。 To solve the multiclass problem, thereby achieving the purpose of cleaning the wall within a short period, the canonical correlation analysis (CCA) classification algorithm had been used. Offline and online experiments were held to analyze/classify EEG signals and use them as real-time commands. The proposed system was efficient in the classification and control phases with an obtained accuracy of 89.92% and had an efficient response speed and timing with a bit rate of 22.23 bits/min. These results suggested that the proposed EEG-based clean robot system is promising for smart home control in terms of completing the tasks of cleaning the walls with efficiency, safety, and robustness. SN - 2040-2295 UR - https://doi.org/10.1155/2020/6968713 DO - 10.1155/2020/6968713 JF - Journal of Healthcare Engineering PB - Hindawi KW - ER -