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Open AccessArticle

Improving the Accuracy and Training Speed of Motor Imagery Brain–Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors

Department of Media Engineering, Catholic University of Korea, 43-1, Yeoggok 2-dong, Wonmmi-gu, Bucheon-si, Gyeonggi-do 14662, Korea
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Sensors 2017, 17(10), 2282; https://doi.org/10.3390/s17102282
Received: 31 August 2017 / Revised: 30 September 2017 / Accepted: 4 October 2017 / Published: 7 October 2017
(This article belongs to the Special Issue Novel Sensors for Bioimaging)
In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain–computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them. Subsequently, the GMM universal background model is trained by the expectation–maximization (EM) algorithm to purify the training data and reduce its size. Finally, a purified and reduced GMM-supervector is used to train the support vector machine classifier. The performance of the proposed method was evaluated for three different motor imagery datasets in terms of accuracy, kappa, mutual information, and computation time, and compared with the state-of-the-art algorithms. The results from the study indicate that the proposed method achieves high accuracy with a small amount of training data compared with the state-of-the-art algorithms in motor imagery EEG classification. View Full-Text
Keywords: brain–computer interface (BCI); electroencephalogram (EEG); training data reduction; support vector machine; motor imagery; wavelet transform brain–computer interface (BCI); electroencephalogram (EEG); training data reduction; support vector machine; motor imagery; wavelet transform
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MDPI and ACS Style

Lee, D.; Park, S.-H.; Lee, S.-G. Improving the Accuracy and Training Speed of Motor Imagery Brain–Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors. Sensors 2017, 17, 2282.

AMA Style

Lee D, Park S-H, Lee S-G. Improving the Accuracy and Training Speed of Motor Imagery Brain–Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors. Sensors. 2017; 17(10):2282.

Chicago/Turabian Style

Lee, David; Park, Sang-Hoon; Lee, Sang-Goog. 2017. "Improving the Accuracy and Training Speed of Motor Imagery Brain–Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors" Sensors 17, no. 10: 2282.

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