A High Performance Scheme for EEG Compression Using a Multichannel Model

  • D. Gopikrishna
  • Anamitra Makur
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2552)


The amount of data contained in electroencephalogram (EEG) recordings is quite massive and this places constraints on bandwidth and storage. The requirement of online transmission of data needs a scheme that allows higher performance with lower computation. Single channel algorithms, when applied on multichannel EEG data fail to meet this requirement. While there have been many methods proposed for multichannel ECG compression, not much work appears to have been done in the area of multichannel EEG compression. In this paper, we present an EEG compression algorithm based on a multichannel model, which gives higher performance compared to other algorithms. Simulations have been performed on both normal and pathological EEG data and it is observed that a high compression ratio with very large SNR is obtained in both cases. The reconstructed signals are found to match the original signals very closely, thus confirming that diagnostic information is being preserved during transmission.


Discrete Cosine Transform High Compression Ratio Neural Network Predictor Single Channel Method Single Channel Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • D. Gopikrishna
    • 1
  • Anamitra Makur
    • 1
  1. 1.Dept. of Electrical Communication EngineeringIndian Institute of ScienceBangaloreIndia

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