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CLC number: TP391.4

On-line Access: 2019-08-29

Received: 2018-02-01

Revision Accepted: 2018-04-24

Crosschecked: 2019-08-13

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Citations:  Bibtex RefMan EndNote GB/T7714


Zhi-chuan Tang


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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.8 P.1087-1098


Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI

Author(s):  Zhi-chuan Tang, Chao Li, Jian-feng Wu, Peng-cheng Liu, Shi-wei Cheng

Affiliation(s):  Industrial Design Institute, Zhejiang University of Technology, Hangzhou 310014, China; more

Corresponding email(s):   ttzzcc@zjut.edu.cn, superli@zju.edu.cn, jianfw@zjut.edu.cn, pliu@cardiffmet.ac.uk

Key Words:  Electroencephalogram (EEG), Motor imagery (MI), Improved common spatial pattern (B-CSP), Feature extraction, Classification

Zhi-chuan Tang, Chao Li, Jian-feng Wu, Peng-cheng Liu, Shi-wei Cheng. Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1087-1098.

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author="Zhi-chuan Tang, Chao Li, Jian-feng Wu, Peng-cheng Liu, Shi-wei Cheng",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI
%A Zhi-chuan Tang
%A Chao Li
%A Jian-feng Wu
%A Peng-cheng Liu
%A Shi-wei Cheng
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 8
%P 1087-1098
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800083

T1 - Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI
A1 - Zhi-chuan Tang
A1 - Chao Li
A1 - Jian-feng Wu
A1 - Peng-cheng Liu
A1 - Shi-wei Cheng
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 8
SP - 1087
EP - 1098
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1800083

Classifying single-trial electroencephalogram (EEG) based motor imagery (MI) tasks is extensively used to control brain-computer interface (BCI) applications, as a communication bridge between humans and computers. However, the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively. In this paper, we propose an improved common spatial pattern (B-CSP) method to extract features for alleviating these adverse effects. First, for different subjects, the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization (ERD) and event-related synchronization (ERS) patterns; then the signals of the optimal frequency band are decomposed into spatial patterns, and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data. The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network (BPNN) classifier to classify single-trial MI EEG. Another two conventional feature extraction methods, original common spatial pattern (CSP) and autoregressive (AR), are used for comparison. An improved classification performance for both data sets (public data set: 91.25%±1.77% for left hand vs. foot and 84.50%±5.42%for left hand vs. right hand; experimental data set: 90.43%±4.26% for left hand vs. foot) verifies the advantages of the B-CSP method over conventional methods. The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively, and this study provides practical and theoretical approaches to BCI applications.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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