Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101285
Title: Multi-channel EEG compression based on matrix and tensor decompositions
Authors: Srinivasan, K.
Dauwels, Justin
Reddy, M. Ramasubba
Cichocki, Andrzej
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2011
Source: Dauwels, J., Srinivasan, K., Reddy, M. R., & Cichocki, A. (2011). Multi-channel EEG compression based on matrix and tensor decompositions. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 629-632.
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Abstract: Compression schemes for EEG signals are developed based on matrix and tensor decomposition. Various ways to arrange EEG signals into matrices and tensors are explored, and several matrix and tensor decomposition schemes are applied, including SVD, CUR, PARAFAC, the Tucker decomposition, and recent random fiber selection approaches. Rate-distortion curves for the proposed matrix and tensor-based EEG compression schemes are computed. It shown that PARAFAC has the best compression performance in this context.
URI: https://hdl.handle.net/10356/101285
http://hdl.handle.net/10220/18354
DOI: http://dx.doi.org/10.1109/ICASSP.2011.5946482
Rights: © 2011 IEEE. This is the author created version of a work that has been peer reviewed and accepted for publication by 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [DOI:http://dx.doi.org/10.1109/ICASSP.2011.5946482].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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