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Energy Efficient Method for Motor Imagery Data Compression

  • Darius BirvinskasEmail author
  • Vacius Jusas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 538)

Abstract

Electroencephalogram (EEG) is a popular method for measuring the electrical activity of the brain, and diagnose a variety of neurological conditions such as epileptic seizure. Furthermore, most Brain - Computer Interface systems provide modes of communication based on EEG, usually 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 low power wireless systems, where low computational complexity and high speed are required.

Keywords

Fast DCT Data compression Electroencephalography EEG 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceKaunas University of TechnologyKaunasLithuania
  2. 2.Department of Software EngineeringKaunas University of TechnologyKaunasLithuania

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