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. 2017 Oct 7;17(10):2282.
doi: 10.3390/s17102282.

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

David Lee  1 , Sang-Hoon Park  2 , Sang-Goog Lee  3
Affiliations

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

David Lee et al. Sensors (Basel). .

Abstract

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.

Keywords: brain–computer interface (BCI); electroencephalogram (EEG); motor imagery; support vector machine; training data reduction; wavelet transform.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Block diagram of motor imagery brain-computer interface (BCI) using the wavelet-based combined feature vector and gaussian mixture model (GMM)-supervector.
Figure 2
Figure 2
GMM-supervector for two features for Subject 7 in dataset IIb of the BCI competition IV.
Figure 3
Figure 3
Classification accuracy based on reduction rate of whole training data on individual subjects.
Figure 4
Figure 4
Computation time for training procedure based on reduction rate of whole training data on individual subjects.
Figure 5
Figure 5
Mean classification accuracy based on selected training data on all subjects.
Figure 6
Figure 6
Computation time for training procedure based on selected training data on all subjects.
See this image and copyright information in PMC

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