An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals
Lossless compression of EEG signal is of great importance for the neurological diagnosis as the specialists consider the exact reconstruction of the signal as a primary requirement. This paper discusses a lossless compression scheme for EEG signals that involves a predictor and an adaptive error mod...
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| Format: | Article |
| Language: | English |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2008
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| Online Access: | http://shdl.mmu.edu.my/2275/ http://shdl.mmu.edu.my/2275/1/732.pdf |
| _version_ | 1848790012155396096 |
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| author | Sriraam, N. Eswaran, C. |
| author_facet | Sriraam, N. Eswaran, C. |
| author_sort | Sriraam, N. |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | Lossless compression of EEG signal is of great importance for the neurological diagnosis as the specialists consider the exact reconstruction of the signal as a primary requirement. This paper discusses a lossless compression scheme for EEG signals that involves a predictor and an adaptive error modeling technique. The prediction residues are arranged based on the error count through an histogram computation. Two optimal regions are identified in the histogram plot through a heuristic search such that the bit requirement for encoding the two regions is minimum. Further improvement in the compression is achieved by removing the statistical redundancy that is present in the residue signal by using a context-based bias cancellation scheme. Three neural network predictors, namely, single-layer perceptron, multilayer perceptron, and Elman network and two linear predictors, namely, autoregressive model and finite impulse response filter are considered. Experiments are conducted using EEG signals recorded under different physiological conditions and the performances of the proposed methods are evaluated in terms of the compression ratio. It is shown that the proposed adaptive error modeling schemes yield better compression results compared to other known compression methods. |
| first_indexed | 2025-11-14T18:05:50Z |
| format | Article |
| id | mmu-2275 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:05:50Z |
| publishDate | 2008 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-22752011-09-09T03:46:40Z http://shdl.mmu.edu.my/2275/ An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals Sriraam, N. Eswaran, C. T Technology (General) QA75.5-76.95 Electronic computers. Computer science Lossless compression of EEG signal is of great importance for the neurological diagnosis as the specialists consider the exact reconstruction of the signal as a primary requirement. This paper discusses a lossless compression scheme for EEG signals that involves a predictor and an adaptive error modeling technique. The prediction residues are arranged based on the error count through an histogram computation. Two optimal regions are identified in the histogram plot through a heuristic search such that the bit requirement for encoding the two regions is minimum. Further improvement in the compression is achieved by removing the statistical redundancy that is present in the residue signal by using a context-based bias cancellation scheme. Three neural network predictors, namely, single-layer perceptron, multilayer perceptron, and Elman network and two linear predictors, namely, autoregressive model and finite impulse response filter are considered. Experiments are conducted using EEG signals recorded under different physiological conditions and the performances of the proposed methods are evaluated in terms of the compression ratio. It is shown that the proposed adaptive error modeling schemes yield better compression results compared to other known compression methods. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 2008-09 Article NonPeerReviewed application/pdf en http://shdl.mmu.edu.my/2275/1/732.pdf Sriraam, N. and Eswaran, C. (2008) An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals. IEEE Transactions on Information Technology in Biomedicine, 12 (5). pp. 587-594. ISSN 1089-7771 http://dx.doi.org/10.1109/TITB.2007.907981 doi:10.1109/TITB.2007.907981 doi:10.1109/TITB.2007.907981 |
| spellingShingle | T Technology (General) QA75.5-76.95 Electronic computers. Computer science Sriraam, N. Eswaran, C. An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals |
| title | An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals |
| title_full | An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals |
| title_fullStr | An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals |
| title_full_unstemmed | An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals |
| title_short | An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals |
| title_sort | adaptive error modeling scheme for the lossless compression of eeg signals |
| topic | T Technology (General) QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2275/ http://shdl.mmu.edu.my/2275/ http://shdl.mmu.edu.my/2275/ http://shdl.mmu.edu.my/2275/1/732.pdf |