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基于脑机接口和物联网的混合信号智能病房协同系统。

Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals.

机构信息

School of Software, South China Normal University, Guangzhou 510631, China.

Pazhou Lab, Guangzhou 510330, China.

出版信息

J Healthc Eng. 2022 Apr 18;2022:6894392. doi: 10.1155/2022/6894392. eCollection 2022.

DOI:10.1155/2022/6894392
PMID:35480157
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9038386/
Abstract

This study proposes a brain-computer interface (BCI)- and Internet of Things (IoT)-based smart ward collaborative system using hybrid signals. The system is divided into hybrid asynchronous electroencephalography (EEG)-, electrooculography (EOG)- and gyro-based BCI control system and an IoT monitoring and management system. The hybrid BCI control system proposes a GUI paradigm with cursor movement. The user uses the gyro to control the cursor area selection and uses blink-related EOG to control the cursor click. Meanwhile, the attention-related EEG signals are classified based on a support-vector machine (SVM) to make the final judgment. The judgment of the cursor area and the judgment of the attention state are reduced, thereby reducing the false operation rate in the hybrid BCI system. The accuracy in the hybrid BCI control system was 96.65 ± 1.44%, and the false operation rate and command response time were 0.89 ± 0.42 events/min and 2.65 ± 0.48 s, respectively. These results show the application potential of the hybrid BCI control system in daily tasks. In addition, we develop an architecture to connect intelligent things in a smart ward based on narrowband Internet of Things (NB-IoT) technology. The results demonstrate that our system provides superior communication transmission quality.

摘要

本研究提出了一种基于混合信号的脑机接口（BCI）和物联网（IoT）的智能病房协作系统。该系统分为混合异步脑电图（EEG）-、眼电图（EOG）-和基于陀螺仪的 BCI 控制系统和物联网监测与管理系统。混合 BCI 控制系统提出了一种具有光标移动功能的图形用户界面（GUI）范例。用户使用陀螺仪控制光标区域选择，并使用眨眼相关的 EOG 控制光标点击。同时，基于支持向量机（SVM）对注意相关的 EEG 信号进行分类，以做出最终判断。光标区域的判断和注意状态的判断都被简化了，从而降低了混合 BCI 系统中的误操作率。混合 BCI 控制系统的准确率为 96.65±1.44%，误操作率和命令响应时间分别为 0.89±0.42 事件/分钟和 2.65±0.48 秒。这些结果表明混合 BCI 控制系统在日常任务中的应用潜力。此外，我们还开发了一种基于窄带物联网（NB-IoT）技术连接智能病房中智能设备的架构。结果表明，我们的系统提供了卓越的通信传输质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/6b167a225419/JHE2022-6894392.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/533e8d680a7d/JHE2022-6894392.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/e9e34ef73401/JHE2022-6894392.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/a007f57916ae/JHE2022-6894392.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/6b167a225419/JHE2022-6894392.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/533e8d680a7d/JHE2022-6894392.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/c1d682d46627/JHE2022-6894392.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/1d22d7908f60/JHE2022-6894392.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/20e42ab8ecb4/JHE2022-6894392.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/8effff523e2e/JHE2022-6894392.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/eb573197eaf0/JHE2022-6894392.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/e9e34ef73401/JHE2022-6894392.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/a007f57916ae/JHE2022-6894392.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5b/9038386/6b167a225419/JHE2022-6894392.009.jpg

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Deep Coupling Recurrent Auto-Encoder with Multi-Modal EEG and EOG for Vigilance Estimation.用于警觉性估计的具有多模态脑电图和眼电图的深度耦合循环自编码器
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