Research Article
Low power compression of EEG signals using JPEG2000
@INPROCEEDINGS{10.4108/ICST.PERVASIVEHEALTH2010.8861, author={Garry Higgins and Brian Mc Ginley and Martin Glavin and Edward Jones}, title={Low power compression of EEG signals using JPEG2000}, proceedings={4th International ICST Conference on Pervasive Computing Technologies for Healthcare}, proceedings_a={PERVASIVEHEALTH}, year={2010}, month={6}, keywords={EEG compression; Wavelets; JPEG2000}, doi={10.4108/ICST.PERVASIVEHEALTH2010.8861} }- Garry Higgins
Brian Mc Ginley
Martin Glavin
Edward Jones
Year: 2010
Low power compression of EEG signals using JPEG2000
PERVASIVEHEALTH
ICST
DOI: 10.4108/ICST.PERVASIVEHEALTH2010.8861
Abstract
This paper outlines a scheme for compressing EEG signals based on the JPEG2000 image compression algorithm. Such a scheme could be used to compress signals in an ambulatory system, where low-power operation is important to conserve battery life; therefore, a high compression ratio is desirable to reduce the amount of data that needs to be transmitted. The JPEG2000 specification makes use of the wavelet transform, which can be efficiently implemented in embedded systems. The standard was broken down to its core components and adapted for use on EEG signals with additional compression steps added. Variations on the components were tested to maximize compression ratio (CR) while maintaining a low percentage root-mean-squared difference (PRD) and minimize power requirements. Initial tests indicate that the algorithm performs well in relation to other EEG compression methods proposed in the literature.