DocumentCode :
617719
Title :
Non-negative matrix factorization for EEG
Author :
Jahan, Ibrahim Salem ; Snasel, Vaclav
Author_Institution :
Comput. Sci. Dept., Tech. Univ. of Ostrava VSB, Ostrava, Czech Republic
fYear :
2013
fDate :
9-11 May 2013
Firstpage :
183
Lastpage :
187
Abstract :
Today with the progress of science and technology becomes signal analysis, data analysis and data mining are very Important in most science and engineering applications. Extracting useful knowledge from experimental raw datasets, measurements, observations and analysis and understand complex data has become very important challenge in the world. The raw datasets in most common is collected from complex phenomena that express to integrated result of various hidden related variables or they are set of underlying hidden component of factors. The complex raw dataset first must be decomposition by dimensionally reduction method such as matrix decomposition to extraction the hidden information or hidden factors of complex raw dataset.
Keywords :
data mining; electroencephalography; feature extraction; matrix decomposition; medical signal processing; EEG; data analysis; data mining; dimensionally reduction method; hidden information extraction; hidden related variables; matrix decomposition; nonnegative matrix factorization; signal analysis; Artificial neural networks; Matrix decomposition; TV; Videos;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013 International Conference on
Conference_Location :
Konya
Print_ISBN :
978-1-4673-5612-1
Type :
conf
DOI :
10.1109/TAEECE.2013.6557219
Filename :
6557219
Link To Document :
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