Joint collaborative representation based sleep stage classification with multi-channel EEG signals

Multi-channel electroencephalography (EEG) signals have been effectively used for sleet staging. However, it is still a challenge to effectively fuse and represent multi-channel EEG features. The coding based feature representation methods, such as sparse representation (SR), have achieved great success in computer vision and pattern recognition. Collaborative representation (CR) is a new coding method, which effectively works as a classifier. In this work, we first employ CR as a feature representation method. Moreover, a new joint CR (JCR) model is proposed for fusing multi-view data, which can represent not only the individual view information, but also the inner-correlative information between multi-views. JCR method is then applied to fuse and represent the features of multi-channel EEG signals for the classification of sleep stages. The experimental results indicate that CR feature outperforms SR feature, and JCR achieves best performance for sleep stage classification by effectively fusing multi-channel EEG signals.