Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data
Electroencephalogram (EEG) is a technique for recording the asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. Artifacts, such as eye blink activities, can corrupt these neuronal signals. While ocular artifact (OA) removal is well investigated for multipl...
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| Format: | Article |
| Language: | English |
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IEEE
2016-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7438792/ |
| _version_ | 1818876759350706176 |
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| author | Saleha Khatun Ruhi Mahajan Bashir I. Morshed |
| author_facet | Saleha Khatun Ruhi Mahajan Bashir I. Morshed |
| author_sort | Saleha Khatun |
| collection | DOAJ |
| description | Electroencephalogram (EEG) is a technique for recording the asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. Artifacts, such as eye blink activities, can corrupt these neuronal signals. While ocular artifact (OA) removal is well investigated for multiple channel EEG systems, in alignment with the recent momentum toward minimalistic EEG systems for use in natural environments, we investigate unsupervised and effective removal of OA from single-channel streaming raw EEG data. In this paper, the unsupervised wavelet transform (WT) decomposition technique was systematically evaluated for the effectiveness of OA removal for a single-channel EEG system. A set of seven raw EEG data set was analyzed. Two commonly used WT methods, Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), were applied. Four WT basis functions, namely, haar, coif3, sym3, and bior4.4, were considered for OA removal with universal threshold and statistical threshold (ST). To quantify OA removal efficacy from single-channel EEG, five performance metrics were utilized: correlation coefficients, mutual information, signal-to-artifact ratio, normalized mean square error, and time-frequency analysis. The temporal and spectral analysis shows that the optimal combination could be DWT with ST with coif3 or bior4.4 to remove OA among 16 combinations. This paper demonstrates that the WT can be an effective tool for unsupervised OA removal from single-channel EEG data for real-time applications. |
| first_indexed | 2024-12-19T13:47:30Z |
| format | Article |
| id | doaj.art-76224bfc6a3f48adaf0c95731d309cc3 |
| institution | Directory Open Access Journal |
| issn | 2168-2372 |
| language | English |
| last_indexed | 2024-12-19T13:47:30Z |
| publishDate | 2016-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj.art-76224bfc6a3f48adaf0c95731d309cc32022-12-21T20:18:50ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722016-01-0141810.1109/JTEHM.2016.25442987438792Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG DataSaleha Khatun0Ruhi Mahajan1Bashir I. Morshed2Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN, USADepartment of Electrical and Computer Engineering, The University of Memphis, Memphis, TN, USADepartment of Electrical and Computer Engineering, The University of Memphis, Memphis, TN, USAElectroencephalogram (EEG) is a technique for recording the asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. Artifacts, such as eye blink activities, can corrupt these neuronal signals. While ocular artifact (OA) removal is well investigated for multiple channel EEG systems, in alignment with the recent momentum toward minimalistic EEG systems for use in natural environments, we investigate unsupervised and effective removal of OA from single-channel streaming raw EEG data. In this paper, the unsupervised wavelet transform (WT) decomposition technique was systematically evaluated for the effectiveness of OA removal for a single-channel EEG system. A set of seven raw EEG data set was analyzed. Two commonly used WT methods, Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), were applied. Four WT basis functions, namely, haar, coif3, sym3, and bior4.4, were considered for OA removal with universal threshold and statistical threshold (ST). To quantify OA removal efficacy from single-channel EEG, five performance metrics were utilized: correlation coefficients, mutual information, signal-to-artifact ratio, normalized mean square error, and time-frequency analysis. The temporal and spectral analysis shows that the optimal combination could be DWT with ST with coif3 or bior4.4 to remove OA among 16 combinations. This paper demonstrates that the WT can be an effective tool for unsupervised OA removal from single-channel EEG data for real-time applications.https://ieeexplore.ieee.org/document/7438792/Artifact RemovalElectroencephalogram (EEG)Ocular ArtifactWavelet TransformSingle Channel EEG |
| spellingShingle | Saleha Khatun Ruhi Mahajan Bashir I. Morshed Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data IEEE Journal of Translational Engineering in Health and Medicine Artifact Removal Electroencephalogram (EEG) Ocular Artifact Wavelet Transform Single Channel EEG |
| title | Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data |
| title_full | Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data |
| title_fullStr | Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data |
| title_full_unstemmed | Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data |
| title_short | Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data |
| title_sort | comparative study of wavelet based unsupervised ocular artifact removal techniques for single channel eeg data |
| topic | Artifact Removal Electroencephalogram (EEG) Ocular Artifact Wavelet Transform Single Channel EEG |
| url | https://ieeexplore.ieee.org/document/7438792/ |
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