Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

Figure 2

Overall signal processing procedure for classification. First, each trial was divided into thirty blocks with a 0.2 s length and 0.1 s overlap. Mean, variance, standard deviation, and skewness were extracted from all blocks and channels. Sequentially, sparse-regression-model-based feature selection was employed to reduce the dimension of the features. All features were used as the input of the trained classifier. Because each trial includes thirty blocks, thirty classifier outputs were acquired; therefore, the label of each trial was determined by selecting the most frequent output of the thirty classifier outputs.