Unsupervised multi-subepoch feature learning and hierarchical classification for EEG-based sleep staging

As the medium of developing brain-computer interface system, the recognition of EEG signals is complicated and difficult due to the complex nonstationary characteristics and the individual difference between subjects. In this paper, we investigate the EEG signal classification problem and propose a novel unsupervised multisubepoch feature learning and hierarchical classification method for automatic sleep staging. First, we divide the EEG epoch into multiple signal subepochs, and each subepoch is mapped to amplitude axis and time axis respectively to obtain two kinds of feature information with amplitude-time dynamic characteristics. Then, the statistical classification features are extracted from the mapped feature information. Furthermore, we conduct unsupervised feature learning for consistent and specific classification features from the perspective of time series. Finally, according to the differences and similarities of EEG signals in different sleep stages, a hierarchical weighted support vector machine-based classification model (H-WSVM) is established, which can use different feature subsets at each classification level and different weighting parameters for unbalanced data samples. To select the optimal feature subset for detecting each sleep stage, we propose a novel evaluation criterion for feature classification ability based on rough set theory. Experimental results on the most commonly used dataset show that the proposed method has better sleep staging performance and can effectively promote the development and application of EEG sleep staging system.