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Frontiers of Information Technology & Electronic Engineering >> 2019, Volume 20, Issue 8 doi: 10.1631/FITEE.1800083

Classification of EEG-based single-trial motor imagery tasks using aB-CSP method forBCI

1. Industrial Design Institute, Zhejiang University of Technology, Hangzhou 310014, China
2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
3. Lincoln Centre for Autonomous Systems, University of Lincoln, Lincoln LN57DH, UK
4. College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310014, China

Available online: 2019-09-23

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Abstract

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.

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