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EEG Data Compression Using Tap9/7 Wavelet Transform and Double Shift-Coding

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New Trends in Information and Communications Technology Applications (NTICT 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1764))

Abstract

The main challenge of transmitting or storing Electroencephalogram (EEG) signals is the size of EEG data recordings. In this paper, an efficient and fast EEG compression system based on bi-orthogonal wavelet transform (Tap9/7) is proposed. It consists of three primary steps: (1) Tap (9/7) wavelet transform, which decomposes the EEG signal into low and multi-high subbands; (2) Progressive hierarchical quantizer, which quantizes wavelet subbands; and (3) double-shift coding, which encodes the input stream with three code-words while using the fewest total bits possible. When evaluating the efficiency of the EEG compression system, the compression ratio (CR) and mean square error (MSE) were the metrics that were utilised. The complexity of the compression system was reduced when using the Tap (9/7) wavelet transform, which also produced superior results in terms of CR and MSE compared to those obtained using the Discrete Wavelet Transform (DCT). The experimental tests are carried out with the CHB-MIT dataset, and the best compression ratio (CR = 7) is accomplished with an error level that is close to zero (MSE = 0.044).

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Author information

Authors and Affiliations

  1. Ministry of Education\General Education Director of Baghdad Karkh-3, Baghdad, Iraq

    Hend A. Hadi

  2. University of Information Technology and Communication (UoITC), Baghdad, Iraq

    Loay E. George

Authors
  1. Hend A. Hadi
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  2. Loay E. George
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Corresponding author

Correspondence to Hend A. Hadi .

Editor information

Editors and Affiliations

  1. University of Information Technology and Communications, Baghdad, Iraq

    Abbas M. Al-Bakry

  2. University of Information Technology and Communications, Baghdad, Iraq

    Safaa O. Al-Mamory

  3. University of Information Technology and Communications, Baghdad, Iraq

    Mouayad A. Sahib

  4. University of Information Technology and Communications, Baghdad, Iraq

    Loay E. George

  5. University of Information Technology and Communications, Baghdad, Iraq

    Jaafar A. Aldhaibani

  6. University of Information Technology and Communications, Baghdad, Iraq

    Haitham S. Hasan

  7. Open University of Tanzania, Dar es Salaam, Tanzania

    George S. Oreku

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Hadi, H.A., George, L.E. (2023). EEG Data Compression Using Tap9/7 Wavelet Transform and Double Shift-Coding. In: Al-Bakry, A.M., et al. New Trends in Information and Communications Technology Applications. NTICT 2022. Communications in Computer and Information Science, vol 1764. Springer, Cham. https://doi.org/10.1007/978-3-031-35442-7_9

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  • DOI: https://doi.org/10.1007/978-3-031-35442-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35441-0

  • Online ISBN: 978-3-031-35442-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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