TY - GEN
T1 - Joint collaborative representation based sleep stage classification with multi-channel EEG signals
AU - Liu, Xiao
AU - Shi, Jun
AU - Tu, Yiheng
AU - Zhang, Zhiguo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84953227660
U2 - 10.1109/EMBC.2015.7318431
DO - 10.1109/EMBC.2015.7318431
M3 - 会议稿件
C2 - 26736331
AN - SCOPUS:84953227660
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 590
EP - 593
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -