Multi-subband and Multi-subepoch Time Series Feature Learning for EEG-based Sleep Stage Classification

EEG plays an important role in the analysis and recognition of brain activity, and which has great potential in the field of biometrics, while EEG-based time series classification is complicated and difficult due to the nonstationary characteristics and individual difference. In this paper, we investigate the EEG signal classification problem and propose a multi-subband and multi-subepoch time series feature learning (MMTSFL) method for automatic sleep stage classification. Specifically, MMTSFL first decomposes multiple subbands with various frequency from raw EEG signals and partitions the obtained subbands in-to multiple consecutive subepochs, and then employs time series feature learning to obtain effective discriminant features. Moreover, amplitude-time based signal features are extracted from each subepoch to represent dynamic variation of EEG signals, and MMTSFL conduct further multipurpose feature learning for specific features, consistent features and temporal features simultaneously. Experiment results on three classification tasks of sleep quality evaluation, fatigue detection and sleep disease diagnosis demonstrate the superiority of the proposed method.