A Novel Feature Fusion Approach for Classification of Motor Imagery EEG Based on Hierarchical Extreme Learning Machine

Because feature extraction from electroencephalogram (EEG) signals is essential for cognitive investigations, effective feature extraction approaches are needed to improve the practical recognition accuracy of EEG signals. In this paper, a strategy is presented for fusing both the linear and nonlinear features from EEG signals to improve the accuracy of motor imagery classification. First, principal component analysis (PCA) is used to extract the linear features from EEG, and linear discriminant analysis (LDA) is introduced to supplement the discriminant features by utilizing the label information of the training data. Second, we use parametric t-distributed stochastic neighbor embedding (PTSNE) to extract the nonlinear features reflecting the original manifold structure of the EEG data. Third, these linear and nonlinear features are fused to generate the final features for classification. After feature extraction, we choose the hierarchical extreme learning machine (HELM) algorithm, which has a high classification accuracy for EEG signal classification of motor imagery. To verify the validity of the strategy, we compare the accuracy of the proposed method with that of other methods on the motor imagery dataset. We achieve a high accuracy of 95.89% and an average accuracy of 93.45%. The performance shows that the accuracy of the proposed feature fusion strategy is effective for classification and that the recognition accuracy is improved compared with other state-of-the-art methods.