Non-negative matrix factorizations of spontaneous electroencephalographic signals for classification.
Non-negative matrix factorization (NMF) is an algorithm that is able to learn a parts-based representation. The paper proposes a new spontaneous EEG classification method for attention-related tasks. NMF was employed as feature extraction tool, which leads to more localized and sparse features than other two reference methods: power spectrum method and principal component analysis. With conventional back propagation neural network classifier, several experiments were carried out. It was showed that the NMF-ANN structure preserved the spatio-temporal characteristics of EEG signals.
Mingyu
Liu
L
STUDENT MEMBER, IEEE, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an, China.
Jue
Wang
W
Chongxun
Zheng
Z