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Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review

  • S.I. : 50Th Anniversary Reviews
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Abstract

Electroencephalography (EEG) is a diagnostic test that records and measures the electrical activity of the human brain. Research investigating human behaviors and conditions using EEG has increased from year to year. Therefore, an efficient approach is vital to process the EEG dataset to improve the output signal quality. The wavelet is one of the well-known approaches for processing the EEG signal in time–frequency domain analysis. The wavelet is better than the traditional Fourier Transform because it has good time–frequency localized properties and multi-resolution analysis where the transient information of an EEG signal can be extracted efficiently. Thus, this review article aims to comprehensively describe the application of the wavelet method in denoising the EEG signal based on recent research. This review begins with a brief overview of the basic theory and characteristics of EEG and the wavelet transform method. Then, several wavelet-based methods commonly applied in EEG dataset denoising are described and a considerable number of the latest published EEG research works with wavelet applications are reviewed. Besides, the challenges that exist in current EEG-based wavelet method research are discussed. Finally, alternative solutions to mitigate the issues are recommended.

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Abbreviations

AC:

Alternating current

AMUSE:

Algorithms for multiple unknown signals extraction

AWICA:

Automatic wavelet independent component analysis

BIT:

Brain imaging tools

BSS:

Blind source separation

EEG:

Electroencephalography

CCA:

Canonical correlation analysis

CWT:

Continuous wavelet transform

DC:

Direct current

DFA:

Detrended fluctuation analysis

DOST:

Discrete orthonormal S-Transform

DWT:

Discrete wavelet transform

ECG:

Electrocardiography

EMD:

Empirical mode decomposition

EMG:

Electromyography

EOG:

Electrooculography

ERP:

Event-related potentials

fMRI:

Functional magnetic resonance imaging

FRWT:

Fractional wavelet transform

FT:

Fourier transform

FWT:

Fast wavelet transform

GSR:

Galvanic skin response

GUI:

Graphical user interface

Hz:

Hertz

IDWT:

Inverse discrete wavelet transform

JADE:

Joint approximate diagonalization

MEG:

Magnetoencephalography

MRI:

Magnetic resonance imaging

MSE:

Mean square error

mV:

Millivolt

NIRS:

Near-infrared spectroscopy

NREM:

Non-rapid eye movement sleep

PCA:

Principal component analysis

PET:

Positron emission tomography

PSNR:

Peak-to-noise signal ratio

SOBI:

Second order blind identification

STFT:

Short-time Fourier transform

SWT:

Stationary wavelet transform

VMD:

Variational mode decomposition

WPD:

Wavelet packet decomposition

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Acknowledgments

The authors would like to acknowledge the financial support provided by the Malaysia Ministry of Higher Education and Universiti Teknologi Malaysia under UTMER grant Q.J130000.2651.17J69 and UTMER grant Q.J130000.3851.20J75. One of the authors, Syarifah Noor Syakiylla Binti Sayed Daud is a Researcher of Universiti Teknologi Malaysia under the Post-Doctoral Fellowship Scheme (Q.J130000.21A2.05E52) for the Project: “Brain Features Extraction using Wavelet Transform Approach for Detection of Visual Memory Improvement and Stress Reduction”.

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  1. School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, 81310, Johor, Malaysia

    Syarifah Noor Syakiylla Sayed Daud & Rubita Sudirman

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  1. Syarifah Noor Syakiylla Sayed Daud
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Correspondence to Syarifah Noor Syakiylla Sayed Daud.

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Daud, S.N.S.S., Sudirman, R. Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review. Ann Biomed Eng 50, 1271–1291 (2022). https://doi.org/10.1007/s10439-022-03053-5

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