Fast DCT algorithms for EEG data compression in embedded systems

Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring and Brain - Computer Interface systems. Usually EEG signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and tran...

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Bibliographic Details
Published inComputer Science and Information Systems Vol. 12; no. 1; pp. 49 - 62
Main Authors Birvinskas, Darius, Jusas, Vacius, Martisius, Ignas, Damasevicius, Robertas
Format Journal Article
LanguageEnglish
Published 2015
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ISSN1820-0214
2406-1018
2406-1018
DOI10.2298/CSIS140101083B

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Summary:Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring and Brain - Computer Interface systems. Usually EEG signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. The least-significant transform coefficients are removed before transmission and are filled with zeros before an inverse transform. We conclude that this method can be used in real-time embedded systems, where low computational complexity and high speed is required. nema
ISSN:1820-0214
2406-1018
2406-1018
DOI:10.2298/CSIS140101083B