Title :
Compressed Sensing Framework for EEG Compression
Author_Institution :
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824
Abstract :
Many applications in signal processing require the efficient representation and processing of data. The traditional approach to efficient signal representation is compression. In recent years, there has been a new approach to compression at the sensing level. Compressed sensing (CS) is an emerging field which is based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper, we propose an application of compressed sensing in the field of biomedical signal processing, particularly electroencophelogram (EEG) collection and storage. A compressed sensing framework is introduced for efficient representation of multichannel, multiple trial EEG data. The proposed framework is based on the revelation that EEG signals are sparse in a Gabor frame. The sparsity of EEG signals in a Gabor frame is utilized for compressed sensing of these signals. A simultaneous orthogonal matching pursuit algorithm is shown to be effective in the joint recovery of the original multiple trail EEG signals from a small number of projections.
Keywords :
Application software; Biomedical signal processing; Compressed sensing; Dictionaries; Electroencephalography; Image reconstruction; Matching pursuit algorithms; Pursuit algorithms; Signal representations; Sparse matrices; Biomedical signal processing; Electroencephalography; Sampling; Signal Reconstruction;
Conference_Titel :
Statistical Signal Processing, 2007. SSP '07. IEEE/SP 14th Workshop on
Conference_Location :
Madison, WI, USA
Print_ISBN :
978-1-4244-1198-6
Electronic_ISBN :
978-1-4244-1198-6
DOI :
10.1109/SSP.2007.4301243