Non-Negative Matrix Factorizations of Spontaneous Electroencephalographic Signals for Classification
Liu Mingyu, Wang Jue, Zheng Chongxun · 2005
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.