dc.contributor.authorDauwels, Justin
dc.contributor.authorSrinivasan, K.
dc.contributor.authorReddy, M. Ramasubba
dc.contributor.authorCichocki, Andrzej
dc.date.accessioned2013-12-20T02:27:33Z
dc.date.available2013-12-20T02:27:33Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationDauwels, 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.en_US
dc.identifier.urihttp://hdl.handle.net/10220/18348
dc.description.abstractVarious 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.en_US
dc.language.isoenen_US
dc.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:http://dx.doi.org/10.1109/ICASSP.2012.6287964 ].en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering
dc.titleMulti-channel EEG compression based on 3D decompositionsen_US
dc.typeConference Paper
dc.contributor.conferenceIEEE International Conference on Acoustics, Speech and Signal Processing (2012 : Kyoto, Japan)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/ICASSP.2012.6287964
dc.description.versionAccepted versionen_US
dc.identifier.rims168271


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