Near-lossless multichannel EEG compression based on matrix and tensor decompositions
A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several mat...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/101371 http://hdl.handle.net/10220/18355 |
| _version_ | 1824454669214154752 |
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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 | A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm. |
| first_indexed | 2025-02-19T03:25:59Z |
| format | Journal Article |
| id | ntu-10356/101371 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:25:59Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1013712020-03-07T14:00:29Z Near-lossless multichannel EEG compression based on matrix and tensor decompositions Srinivasan, K. Dauwels, Justin Reddy, M. Ramasubba Cichocki, Andrzej School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm. Accepted version 2013-12-20T06:13:20Z 2019-12-06T20:37:23Z 2013-12-20T06:13:20Z 2019-12-06T20:37:23Z 2013 2013 Journal Article Dauwels, J., Srinivasan, K., Reddy, M. R., & Cichocki, A. (2013). Near-lossless multichannel EEG compression based on matrix and tensor decompositions. IEEE journal of biomedical and health informatics, 17(3), 708-714. https://hdl.handle.net/10356/101371 http://hdl.handle.net/10220/18355 10.1109/TITB.2012.2230012 170098 en IEEE journal of biomedical and health informatics © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TITB.2012.2230012]. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Srinivasan, K. Dauwels, Justin Reddy, M. Ramasubba Cichocki, Andrzej Near-lossless multichannel EEG compression based on matrix and tensor decompositions |
| title | Near-lossless multichannel EEG compression based on matrix and tensor decompositions |
| title_full | Near-lossless multichannel EEG compression based on matrix and tensor decompositions |
| title_fullStr | Near-lossless multichannel EEG compression based on matrix and tensor decompositions |
| title_full_unstemmed | Near-lossless multichannel EEG compression based on matrix and tensor decompositions |
| title_short | Near-lossless multichannel EEG compression based on matrix and tensor decompositions |
| title_sort | near lossless multichannel eeg compression based on matrix and tensor decompositions |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/101371 http://hdl.handle.net/10220/18355 |
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