Research On EEG-controlled Smart Home System Based On Deep Learning

With the rapid development of Internet information technology,as the embodiment of the Internet under the influence of the Internet,smart home systems are gradually emerging.Most smart home control systems currently use voice,gesture,smart phone or Web page control schemes.However,these existing programs do not really help those with disabilities who are physically disabled or physically disabled.In this paper,we propose a new EEG-based intelligent home system based on deep learning.The results of the thesis are mainly reflected in three aspects.Firstly,in order to reduce the time-consuming preprocessing and feature extraction in EEG signal processing and improve the accuracy of EEG signal recognition,a brainwave information processing scheme based on deep learning model is proposed,which does not require time-consuming and labor-intensive data.Pre-processing and empirical feature extraction enable accurate multi-classification of motor imagery EEG signals.Secondly,a smart home solution for people with disabilities such as the elderly or physically disabled is proposed.After the user collects and trains the EEG deep learning model,the intelligent control of various household items can be realized.Finally,this paper designs a set of EEG intelligent home system based on deep learning,which verifies the feasibility and practicability of the design.