Classification of EEG signal using convolutional neural networks

被引：0

作者：

Wang, Jianhua ^{[1
]}

Yu, Gaojie ^{[1
]}

Zhong, Liu ^{[1
]}

Chen, Weihai ^{[1
]}

Sun, Yu ^{[2
]}

机构：

[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China

[2] Zhejiang Univ, Dept Biomed Engn, Hangzhou 310027, Peoples R China

来源：

PROCEEDINGS OF THE 2019 14TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2019) | 2019年

基金：

国家重点研发计划; 中国国家自然科学基金;

关键词：

Electroencephalography (EEG); Motor Imagery (MI); Convolutional Neural Network (CNN); SINGLE-TRIAL EEG; MOTOR IMAGERY;

D O I：

10.1109/iciea.2019.8834381

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Electroencephalography (EEG) signal recorded during motor imagery (MI) has been widely applied in noninvasive brain-computer interfaces (Bats) as a communication approach. As an important issue in BCI systems, signal classification has been attached to increasingly attention. This paper presents a new classification method based on the deep convolutional neural network (CNN) for MI-EEG. Compared with other three classification methods (LDA, SVM, MLP), the results demonstrate that CNN can provide better classification performance. The present study shows that the proposed method is effective to classify MI and have potential to be a proper choice for BCI applications. The proposed paradigm could be further implemented by optimizing the network structure.

引用

页码：1694 / 1698

页数：5

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