Non negative Matriz Factorization and Its Application in EEG Signal Processing
Non-negative Matrix Factorization (NMF) is a method to obtain a representation of data using non-negativity constraints. These constraints lead to a part-based representation in the vector space because they allow only additive, not subtractive, combinations of original data. This is how NMF learns a partbased representation. This paper introduces briefly the theory and algorithm of NMF. Then a NMFANN framework is presented to classify spontaneous EEG in five metal tasks. Several comparisons and experiments were carried out. The results showed that NMF lead more localized and sparse features than power spectrum method and principal component analysis method did, and that the NMF-ANN structure preserved the spatio-temporal characteristics of EEG signals. Its best cognition rate of five mental task pairs can achieves better than 88.0％. It may be a promising classifier for Brain Computer Interface (BCI) scheme.
EEG Non-negative Matriz Factorization Artificial Neural Network Mental Task
Liu Mingyu Ji Hongbing Zhao Chunhong
School of Electronic Engineering, Xidian University, Xian, Shaanxi, China School of Mechatronic Engineering, Northwestern Polytechnical University, Xian, Shaanxi, China
国际会议
上海
英文
2146-2148
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）