Classification of imagined speech EEG signals based on feature fusion

被引：0

作者：

Zhang L.-W. ^{[1
]}

Zhou Z.-D. ^{[1
]}

Xu Y.-F. ^{[1
]}

Wang J.-W. ^{[1
]}

Ji W.-T. ^{[1
]}

Song Z.-F. ^{[1
]}

机构：

[1] College of Aeronautics, Nanjing University of Aeronautics and Astronautics, Nanjing

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2023年 / 57卷 / 04期

关键词：

brain-computer interface (BCI); discrete wavelet transform (DWT); electroencephalogram; empirical mode decomposition (EMD); imagined speech;

D O I：

10.3785/j.issn.1008-973X.2023.04.010

中图分类号：

学科分类号：

摘要：

A feature extraction and classification method of imagined speech electroencephalogram (EEG) signals was proposed by combining discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in order to improve the accuracy of imagined speech brain-computer interface (BCI) control task. DWT and EMD were applied to the original imagined speech EEG signals respectively, and the features of the signal of each channel were extracted and fused. Then the RBF support vector machine (SVM) was used to classify the imagined speech EEG signals. The experimental results show that the classification accuracy can achieve an average by 82.46% with the proposed method, which is 20.77% higher than that with the DWT method, and 21.12% higher than that with the EMD method. The proposed method can effectively improve the classification accuracy of imagined speech EEG signals, and is of great value to the practical application of imagined speech BCI. © 2023 Zhejiang University. All rights reserved.

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页码：726 / 734

页数：8

共 33 条

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