Open AccessResearch on the Key Technologies of Motor Imagery EEG Signal Based on Deep Learning
Author(s) -
Zhuozheng Wang,
Zhuo Ma,
Xiuwen Du,
Yuan Dong,
Wei Liu
Publication year - 2020
Publication title -
journal of autonomous intelligence
Language(s) - English
Resource type - Journals
ISSN - 2630-5046
DOI - 10.32629/jai.v2i4.60
Subject(s) - motor imagery , brain–computer interface , computer science , electroencephalography , artificial intelligence , convolutional neural network , pattern recognition (psychology) , preprocessor , deep learning , signal (programming language) , interface (matter) , speech recognition , psychology , bubble , psychiatry , maximum bubble pressure method , parallel computing , programming language
Brain-computer interface (BCI) is an emerging area of research that establishes a connection between the brain and external devices in a completely new way. It provides a new idea about the rehabilitation of brain diseases, human-computer interaction and augmented reality. One of the main problems of implementing BCI is to recognize and classify the motor imagery Electroencephalography(EEG) signals effectively. This paper takes the characteristic data of motor imagery of EEG as the research object to conduct the research of multi-classification method. In this study, we use the Emotiv helmet with 16 biomedical sensors to obtain EEG signal, adopt the fast independent component analysis and the fast Fourier transform to realize signal preprocessing and select the common spatial pattern algorithm to extract the features of the motor imagery EEG signal. In order to improve the accuracy of recognition of EEG signal, a new deep learning network is designed for multi-channel self-acquired EEG data set which is named as min-VGG-LSTMnet in this paper. This network combines Long Short-Term Memory Network with convolutional neural network VGG and achieves the four-classification task of the left-hand, right-hand, left-foot and right-foot lifting movements based on motor imagery. The results show that the accuracy of the proposed classification method is at least 8.18% higher than other mainstream deep-learning methods.