Referenced Items are not available for this document.
In this paper, a method is proposed to compress multi-channel electroencephalographic (EEG) signals in a scalable fashion. Correlation between EEG channels is exploited through clustering using a k-means method. Representative channels for each of the clusters are encoded individually while other channels are encoded differentially, i.e., with respect to their respective cluster representatives. The compression is performed using the embedded zero-tree wavelet encoding adapted to 1-D signals. Simulations show that the scalable features of the scheme lead to a flexible quality/rate tradeoff, without requiring detailed EEG signal modeling.
Published in:
Information Technology in Biomedicine, IEEE Transactions on
(Volume:15
,
Issue:
6
)
Date of Publication: Nov. 2011
- Page(s):
- 831 - 838
- ISSN :
- 1089-7771
- INSPEC Accession Number:
- 12376500
- Digital Object Identifier :
- 10.1109/TITB.2011.2171703
- Product Type:
- Journals & Magazines
- Date of Publication :
- 13 October 2011
- Date of Current Version :
- 21 November 2011
- Issue Date :
- Nov. 2011
- Sponsored by :
- IEEE Computer Society [Technical Co-Sponsor]
- PubMed ID :
- 21997286
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