Fast DCT Algorithms for EEG Data Compression in Embedded Systems

Darius Birvinskas1, Vacius Jusas1, Ignas Martisius1 and Robertas Damasevicius1

  1. Software Engineering Department, Kaunas University of Technology
    Studentu St. 50 - 404, Kaunas, Lithuania
    darius.birvinskas@ktu.lt

Abstract

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.

Key words

Fast DCT, data compression, electroencephalography

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS140101083B

Publication information

Volume 12, Issue 1 (January 2015)
Year of Publication: 2015
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Birvinskas, D., Jusas, V., Martisius, I., Damasevicius, R.: Fast DCT Algorithms for EEG Data Compression in Embedded Systems. Computer Science and Information Systems, Vol. 12, No. 1, 49–62. (2015)