摘要
为实现家居系统的智能控制,提出一种基于脑机接口、脑电信号识别分类和增强现实(AR:Augmented Reality)的解决方案。通过佩戴设备收集提取脑电图(EEG:Electro Encephalo Gram)信号,对数据使用小波变换去噪并利用短时傅立叶变换进一步处理,利用主成分分析(PCA:Principal Component Analysis)进行降维和卷积神经网络(CNN:Convolutional Neural Network)进行分类,形成分类模型。根据分类结果得到大脑发出的指令,以此对家居进行控制,结合AR技术能使控制过程可视化且更具交互性,符合未来智能家居控制方法的发展趋势。
In order to achieve the intelligent control of home system,a solution based on brain computer interface,recognition and classification of brain electrical signals and AR(Augmented Reality)is proposed.EEG(Electro Encephalo Gram)signals are collected and extracted from the brain by wearing a device,and the data is de-noised by wavelet transform and further processed by STFT(Short Time Fourier Transform).Then,PCA(Principal Component Analysis)and CNN(Convolutional Neural Network)are used for classification to form a classification model.According to the classification results,the instructions is obtained from the brain to control the home.Combined with AR technology,the control process can be visualized and is more interactive,which is in line with the development trend of the control method of the future smart home.
作者
王增尉
刘佳奇
戴露
李芷萱
王启月
李蛟
赵宏伟
WANG Zengwei;LIU Jiaqi;DAI Lu;LI Zhixuan;WANG Qiyue;LI Jiao;ZHAO Hongwei(College of Electronic Science and Technology,Jilin University,Changchun 130012,China;College of Computer Science and Technology,Jilin University,Changchun 130012,China;Library,Jilin University,Changchun 130012,China)
出处
《吉林大学学报（信息科学版）》
CAS
2021年第1期121-126,共6页
Journal of Jilin University（Information Science Edition）
基金
吉林省科技发展计划技术攻关基金资助项目(20190302026GX)
吉林省自然科学基金资助项目(20200201037JC)
吉林省高等教育学会高教科研基金资助项目(JGJX2018D10)。
关键词
脑机接口
智能家居
EEG信号
卷积神经网络
AR技术
brain computer interface
smart home
the electro encephalo gram(EEG)signal
convolutional neural network(CNN)
augmented reality(AR)technology
