Speeding up SVM training in brain-computer interfaces.Traditional Support Vector Machine (SVM) is widely used classification method for brain-computer interface (BCI).  However, SVM has a high computational complexity.  Finally, a nonlinear SVM classifier is used to classify the reduced training data. 
SVM
In this paper, Gaussian Mixture Model (GMM)-based training data reduction is proposed to reduce high computational complexity.  Thereafter, the GMM is implemented to reduce training data sizes. 
GMM
The proposed method is configured as follows: First, wavelet-based combined feature vectors are applied for motor imagery electroencephalography (EEG) identification and principal component analysis (PCA) are used to reduce the dimension of feature vectors. 
PCA
Speeding up SVM training in brain-computer interfaces.Traditional Support Vector Machine (SVM) is widely used classification method for brain-computer interface (BCI).  However, SVM has a high computational complexity.  Finally, a nonlinear SVM classifier is used to classify the reduced training data. 
SVM