Multichannel EEG compression : wavelet-based image and volumetric coding approach
In this paper, lossless and near-lossless compression algorithms for multichannel electroencephalogram (EEG) signals are presented based on image and volumetric coding. Multichannel EEG signals have significant correlation among spatially adjacent channels; moreover, EEG signals are also correlated...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/101614 http://hdl.handle.net/10220/18349 |
| _version_ | 1826126895085780992 |
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| author | Srinivasan, K. Dauwels, Justin Reddy, M. Ramasubba |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Srinivasan, K. Dauwels, Justin Reddy, M. Ramasubba |
| author_sort | Srinivasan, K. |
| collection | NTU |
| description | In this paper, lossless and near-lossless compression algorithms for multichannel electroencephalogram (EEG) signals are presented based on image and volumetric coding. Multichannel EEG signals have significant correlation among spatially adjacent channels; moreover, EEG signals are also correlated across time. Suitable representations are proposed to utilize those correlations effectively. In particular, multichannel EEG is represented either in the form of image (matrix) or volumetric data (tensor), next a wavelet transform is applied to those EEG representations. The compression algorithms are designed following the principle of “lossy plus residual coding,” consisting of a wavelet-based lossy coding layer followed by arithmetic coding on the residual. Such approach guarantees a specifiable maximum error between original and reconstructed signals. The compression algorithms are applied to three different EEG datasets, each with different sampling rate and resolution. The proposed multichannel compression algorithms achieve attractive compression ratios compared to algorithms that compress individual channels separately. |
| first_indexed | 2024-10-01T07:00:13Z |
| format | Journal Article |
| id | ntu-10356/101614 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:00:13Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1016142020-03-07T14:00:33Z Multichannel EEG compression : wavelet-based image and volumetric coding approach Srinivasan, K. Dauwels, Justin Reddy, M. Ramasubba School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering In this paper, lossless and near-lossless compression algorithms for multichannel electroencephalogram (EEG) signals are presented based on image and volumetric coding. Multichannel EEG signals have significant correlation among spatially adjacent channels; moreover, EEG signals are also correlated across time. Suitable representations are proposed to utilize those correlations effectively. In particular, multichannel EEG is represented either in the form of image (matrix) or volumetric data (tensor), next a wavelet transform is applied to those EEG representations. The compression algorithms are designed following the principle of “lossy plus residual coding,” consisting of a wavelet-based lossy coding layer followed by arithmetic coding on the residual. Such approach guarantees a specifiable maximum error between original and reconstructed signals. The compression algorithms are applied to three different EEG datasets, each with different sampling rate and resolution. The proposed multichannel compression algorithms achieve attractive compression ratios compared to algorithms that compress individual channels separately. Accepted version 2013-12-20T02:44:11Z 2019-12-06T20:41:28Z 2013-12-20T02:44:11Z 2019-12-06T20:41:28Z 2012 2012 Journal Article Srinivasan, K., Dauwels, J., & Reddy, M. R. (2013). Multichannel EEG compression : wavelet-based image and volumetric coding approach. IEEE journal of biomedical and health informatics, 17(1), 113-120. 2168-2194 https://hdl.handle.net/10356/101614 http://hdl.handle.net/10220/18349 10.1109/TITB.2012.2194298 168413 en IEEE journal of biomedical and health informatics © 2012 IEEE. This is the author created version of a work that has been peer reviewed and accepted for publication by IEEE Journal of Biomedical and Health informatics, 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/TITB.2012.2194298 ]. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Srinivasan, K. Dauwels, Justin Reddy, M. Ramasubba Multichannel EEG compression : wavelet-based image and volumetric coding approach |
| title | Multichannel EEG compression : wavelet-based image and volumetric coding approach |
| title_full | Multichannel EEG compression : wavelet-based image and volumetric coding approach |
| title_fullStr | Multichannel EEG compression : wavelet-based image and volumetric coding approach |
| title_full_unstemmed | Multichannel EEG compression : wavelet-based image and volumetric coding approach |
| title_short | Multichannel EEG compression : wavelet-based image and volumetric coding approach |
| title_sort | multichannel eeg compression wavelet based image and volumetric coding approach |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/101614 http://hdl.handle.net/10220/18349 |
| work_keys_str_mv | AT srinivasank multichanneleegcompressionwaveletbasedimageandvolumetriccodingapproach AT dauwelsjustin multichanneleegcompressionwaveletbasedimageandvolumetriccodingapproach AT reddymramasubba multichanneleegcompressionwaveletbasedimageandvolumetriccodingapproach |