title: Non-negative matrix factorizations of spontaneous electroencephalographic signals for classification.
abstract: 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.
authors.affiliation: STUDENT MEMBER, IEEE, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an, China.
authors.forename: Liu
authors.initials: L
authors.lastname: Mingyu
authors.forename: Wang
authors.initials: W
authors.lastname: Jue
authors.forename: Zheng
authors.initials: Z
authors.lastname: Chongxun