Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine

Electroencephalogram (EEG) signals play an important role in clinical diagnosis and cognitive neuroscience. Automatic classification of EEG signals is gradually becoming the research focus, which contains two procedures: feature extraction and classification. In the phase of feature extraction, a hybrid feature extraction method is proposed and the features are derived by performing linear and nonlinear feature extraction methods, which can describe abundant properties of original EEG signals. In order to eliminate irrelevant and redundant features, feature selection based on class separability is employed to select the optimal feature subset. In the phase of classification, this paper presents a novel ensemble extreme learning machine based on linear discriminant analysis. Linear discriminant analysis is used to transform training subsets that are generated by bootstrap method, through which we can increase the differences of basic classifiers and reduce generalization errors of ensemble extreme learning machine. Experiments on two different EEG datasets are conducted in this study. Class separability is investigated to verify the effectiveness of feature extraction methods. The overall classification results show that compared with other similar studies, the proposed method can significantly enhance the performance of EEG signals classification.