Multi-channel EEG compression based on 3D decompositions
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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/101286 http://hdl.handle.net/10220/18348 |
| _version_ | 1826122149996265472 |
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| author | Srinivasan, K. Dauwels, Justin Reddy, M. Ramasubba Cichocki, Andrzej |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Srinivasan, K. Dauwels, Justin Reddy, M. Ramasubba Cichocki, Andrzej |
| author_sort | Srinivasan, K. |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:43:47Z |
| format | Conference Paper |
| id | ntu-10356/101286 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:43:47Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1012862020-03-07T13:24:50Z Multi-channel EEG compression based on 3D decompositions Srinivasan, K. Dauwels, Justin Reddy, M. Ramasubba Cichocki, Andrzej School of Electrical and Electronic Engineering IEEE International Conference on Acoustics, Speech and Signal Processing (2012 : Kyoto, Japan) DRNTU::Engineering::Electrical and electronic engineering 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. Accepted version 2013-12-20T02:27:33Z 2019-12-06T20:36:06Z 2013-12-20T02:27:33Z 2019-12-06T20:36:06Z 2012 2012 Conference Paper 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. https://hdl.handle.net/10356/101286 http://hdl.handle.net/10220/18348 10.1109/ICASSP.2012.6287964 168271 en © 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 ]. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Srinivasan, K. Dauwels, Justin Reddy, M. Ramasubba Cichocki, Andrzej Multi-channel EEG compression based on 3D decompositions |
| title | Multi-channel EEG compression based on 3D decompositions |
| title_full | Multi-channel EEG compression based on 3D decompositions |
| title_fullStr | Multi-channel EEG compression based on 3D decompositions |
| title_full_unstemmed | Multi-channel EEG compression based on 3D decompositions |
| title_short | Multi-channel EEG compression based on 3D decompositions |
| title_sort | multi channel eeg compression based on 3d decompositions |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/101286 http://hdl.handle.net/10220/18348 |
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