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Title: Multi-channel EEG compression based on 3D decompositions
Authors: Srinivasan, K.
Dauwels, Justin
Reddy, M. Ramasubba
Cichocki, Andrzej
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Dauwels, J., Srinivasan, K., Reddy, M. R., & Cichocki, A. (2012). Multi-channel EEG compression based on 3D decompositions. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 637-640.
Abstract: Various compression algorithms for multi-channel electroencephalograms (EEG) are proposed and compared. The multi-channel EEG is represented as a three-way tensor (or 3D volume) to exploit both spatial and temporal correlations efficiently. A general two-stage coding framework is developed for multi-channel EEG compression. In the first stage, we consider (i) wavelet-based volumetric coding; (ii) energy-based lossless compression of wavelet subbands; (iii) tensor decomposition based coding. In the second stage, the residual is quantized and coded. Through such two-stage approach, one can control the maximum error (worst-case distortion). Numerical results for a standard EEG data set show that tensor-based coding achieves lower worst-case error and comparable average error than the wavelet- and energy-based schemes.
DOI: 10.1109/ICASSP.2012.6287964
Rights: © 2012 IEEE. This is the author created version of a work that has been peer reviewed and accepted for publication by 2012 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: ].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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