Non negative Matrix 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 part- based 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.

[1]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[2]  I. Jolliffe.  Principal Component Analysis , 2002 .

[3]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[4]  Z. Keirn,et al.  A new mode of communication between man and his surroundings , 1990, IEEE Transactions on Biomedical Engineering.

[5]  Stefan M. Wild,et al.  Improving non-negative matrix factorizations through structured initialization , 2004, Pattern Recognit..

[6]  Ralf Möller,et al.  Coupled principal component analysis , 2004, IEEE Transactions on Neural Networks.

[7]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[8]  Michael T. Manry,et al.  A neural network training algorithm utilizing multiple sets of linear equations , 1996, Conference Record of The Thirtieth Asilomar Conference on Signals, Systems and Computers.