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

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Title: Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review.
Authors: Daud, Syarifah Noor Syakiylla Sayed1 (AUTHOR) sya.syakiylla@gmail.com, Sudirman, Rubita1 (AUTHOR)
Source: Annals of Biomedical Engineering. Oct2022, Vol. 50 Issue 10, p1271-1291. 21p.
Subjects: Electroencephalography, Signal processing, Wavelet transforms, Time-frequency analysis, Signal denoising, Receiver operating characteristic curves
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. [ABSTRACT FROM AUTHOR]
Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review.
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  Data: <searchLink fieldCode="JN" term="%22Annals+of+Biomedical+Engineering%22">Annals of Biomedical Engineering</searchLink>. Oct2022, Vol. 50 Issue 10, p1271-1291. 21p.
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  Data: <searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelet+transforms%22">Wavelet transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Time-frequency+analysis%22">Time-frequency analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+denoising%22">Signal denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10439-022-03053-5
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        Text: English
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      – SubjectFull: Electroencephalography
        Type: general
      – SubjectFull: Signal processing
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      – SubjectFull: Wavelet transforms
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      – SubjectFull: Time-frequency analysis
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      – SubjectFull: Signal denoising
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      – SubjectFull: Receiver operating characteristic curves
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      – TitleFull: Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review.
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              Text: Oct2022
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              Y: 2022
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