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Title: Compressive sampling of EEG signals with finite rate of innovation
Authors: Poh, K.-K 
Marziliano, P
Keywords: Compression methods
Compression scheme
Compressive sampling
Computational resources
EEG signals
Electroencephalographic signals
Finite rate
Fourier coefficients
Long-term recording
Nyquist rate
Original signal
Reconstruction error
Sampling devices
Sampling theory
Wavelet compression
Quality assurance
Signal reconstruction
Signal processing
Issue Date: 2010
Publisher: Hindawi
Citation: Poh, K.-K, Marziliano, P (2010). Compressive sampling of EEG signals with finite rate of innovation. Eurasip Journal on Advances in Signal Processing 2010 : 183105. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Analyses of electroencephalographic signals and subsequent diagnoses can only be done effectively on long term recordings that preserve the signals' morphologies. Currently, electroencephalographic signals are obtained at Nyquist rate or higher, thus introducing redundancies. Existing compression methods remove these redundancies, thereby achieving compression. We propose an alternative compression scheme based on a sampling theory developed for signals with a finite rate of innovation (FRI) which compresses electroencephalographic signals during acquisition. We model the signals as FRI signals and then sample them at their rate of innovation. The signals are thus effectively represented by a small set of Fourier coefficients corresponding to the signals' rate of innovation. Using the FRI theory, original signals can be reconstructed using this set of coefficients. Seventy-two hours of electroencephalographic recording are tested and results based on metrices used in compression literature and morphological similarities of electroencephalographic signals are presented. The proposed method achieves results comparable to that of wavelet compression methods, achieving low reconstruction errors while preserving the morphologiies of the signals. More importantly, it introduces a new framework to acquire electroencephalographic signals at their rate of innovation, thus entailing a less costly low-rate sampling device that does not waste precious computational resources. Copyright © 2010 K.-K. Poh and P. Marziliano.
Source Title: Eurasip Journal on Advances in Signal Processing
ISSN: 1687-6172
DOI: 10.1155/2010/183105
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications

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