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Dictionary selection for compressed sensing of EEG signals using sparse binary matrix and spatiotemporal sparse

Manika Rani Dey1, Arsam Shiraz2, Saeed Sharif1

  • 1Department of Engineering and Computing, University of East London, E16 2RD, United Kingdom.

Biomedical Physics & Engineering Express
|January 30, 2022
PubMed
Summary

Compressed sensing (CS) offers energy-efficient online electroencephalogram (EEG) monitoring. This study identifies the Beylkin wavelet dictionary as optimal for CS-based EEG signal processing, especially at high compression ratios.

Keywords:
DWTEEGcompressed sensingdictionarysignal reconstruction

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Neuroscience

Background:

  • Online electroencephalogram (EEG) monitoring faces challenges with high data volume and power consumption.
  • Compressed sensing (CS) offers a potential solution for efficient bio-signal monitoring.
  • EEG signals lack strong temporal correlation, necessitating sparsifying dictionaries for effective CS.

Purpose of the Study:

  • To systematically evaluate various wavelet bases as sparsifying dictionaries for CS-based EEG monitoring.
  • To identify the optimal dictionary for enhancing the performance of CS for EEG signals.
  • To compare the performance of selected dictionaries against no dictionary and discrete cosine transform (DCT).

Main Methods:

  • Systematic evaluation of different wavelet bases (e.g., Beylkin) as sparsifying dictionaries for CS.
  • Utilizing real multichannel EEG data from 15 subjects.
  • Assessing dictionary performance based on attributes like vanishing moments and coherence with the sensing matrix.

Main Results:

  • The Beylkin wavelet dictionary demonstrated superior performance in the CS framework for EEG signals.
  • Dictionary-based CS (Beylkin and DCT) showed tangible improvements primarily at high compression ratios (80%) and smaller block sizes.
  • Performance gains were marginal when using dictionaries at lower compression ratios compared to no dictionary.

Conclusions:

  • The Beylkin wavelet dictionary is recommended for CS-based EEG monitoring to optimize performance.
  • Sparsifying dictionaries offer benefits for EEG CS, particularly under high compression and specific block size constraints.
  • The choice of dictionary and compression parameters significantly impacts the efficiency and accuracy of EEG monitoring systems.