Lossless and near-lossless compression of EEG signals

Judit Cinkler, Xuan Kong, Nasir Memon

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we study compression techniques for electroencelograph (EEG) signals. A variety of lossless compression techniques, ranging from simple dictionary based approaches to more sophisticated context modeling techniques based on recent work in lossless image coding are investigated and compared. It is seen that compression ratios obtained by lossless compression are limited. Though lossy compression can yield significantly higher compression ratios while potentially preserving diagnostic accuracy, it is not usually employed due to legal concerns. Hence, we investigate near-lossless compression techniques that give quantitative bounds on the errors introduced during compression. It is observed that such techniques give significantly higher compression ratios. Simulation results with a large variety of data sets are reported.

Original languageEnglish (US)
Title of host publicationConference Record of the Asilomar Conference on Signals, Systems and Computers
EditorsM.P. Farques, R.D. Hippenstiel
PublisherIEEE Comp Soc
Number of pages5
StatePublished - 1998
EventProceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA
Duration: Nov 2 1997Nov 5 1997


OtherProceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2)
CityPacific Grove, CA, USA

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Electrical and Electronic Engineering


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