title: Nonnegative tensor factorization for continuous EEG classification.
abstract: In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.
authors: Department of Computer Science, Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang 790-784, Republic of Korea. leehk@postech.ac.kr
authors: Hyekyoung
authors: H
authors: Lee
authors: Yong-Deok
authors: YD
authors: Kim
authors: Andrzej
authors: A
authors: Cichocki
authors: Seungjin
authors: S
authors: Choi
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D004569:Electroencephalography / Q000145:classification*; D006801:Humans; D014584:User-Computer Interface