Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis
Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/32566 |
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| _version_ | 1864513443692806144 |
|---|---|
| author | Abu Farha, Nadia |
| author2 | Al-Shargie, Fares Tariq, Usman Al-Nashash, Hasan |
| author2_role | author author author |
| author_facet | Abu Farha, Nadia Al-Shargie, Fares Tariq, Usman Al-Nashash, Hasan |
| author_role | author |
| dc.creator.none.fl_str_mv | Abu Farha, Nadia Al-Shargie, Fares Tariq, Usman Al-Nashash, Hasan |
| dc.date.none.fl_str_mv | 2022-04-15 2025-12-17T10:46:41Z 2025-12-17T10:46:41Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Abu Farha, N., Al-Shargie, F., Tariq, U., & Al-Nashash, H. (2022). Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis. Sensors, 22(8), 3051. https://doi.org/10.3390/s22083051 1424-8220 https://hdl.handle.net/11073/32566 10.3390/s22083051 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | https://doi.org/10.3390/s22083051 |
| dc.rights.none.fl_str_mv | Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| dc.subject.none.fl_str_mv | Vigilance Assessment Noise Feature Extraction Dimensionality Reduction Thresholds Wavelet Transform Independent Component Analysis |
| dc.title.none.fl_str_mv | Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis |
| dc.type.none.fl_str_mv | Published version Peer-Reviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance. Unfortunately, EEG signals are prone to artifacts due to eye movement, muscle contraction, and electrical noise. Mitigating these artifacts is important for an accurate vigilance level assessment. Independent Component Analysis (ICA) is an effective method and has been extensively used in the suppression of EEG artifacts. However, in vigilance assessment applications, it was found to suffer from leakage of the cerebral activity into artifacts. In this work, we show that the wavelet ICA (wICA) method provides an alternative for artifact reduction, leading to improved vigilance level assessment results. We conducted an experiment in nine human subjects to induce two vigilance states, alert and vigilance decrement, while performing a Stroop Color–Word Test for approximately 45 min. We then compared the performance of the ICA and wICA preprocessing methods using five classifiers. Our classification results showed that in terms of features extraction, the wICA method outperformed the existing ICA method. In the delta, theta, and alpha bands, we obtained a mean classification accuracy of 84.66% using the ICA method, whereas the mean accuracy using the wICA methodwas 96.9%. However, no significant improvement was observed in the beta band. In addition, we compared the topographical map to show the changes in power spectral density across the brain regions for the two vigilance states. The proposed method showed that the frontal and central regions were most sensitive to vigilance decrement. However, in this application, the proposed wICA shows a marginal improvement compared to the Fast-ICA. |
| format | article |
| id | aus_3df79f5d6f1a83b70093137ce638d0e7 |
| identifier_str_mv | Abu Farha, N., Al-Shargie, F., Tariq, U., & Al-Nashash, H. (2022). Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis. Sensors, 22(8), 3051. https://doi.org/10.3390/s22083051 1424-8220 10.3390/s22083051 |
| language_invalid_str_mv | en |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/32566 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | MDPI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| spelling | Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component AnalysisAbu Farha, NadiaAl-Shargie, FaresTariq, UsmanAl-Nashash, HasanVigilance AssessmentNoiseFeature ExtractionDimensionality ReductionThresholdsWavelet TransformIndependent Component AnalysisVigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance. Unfortunately, EEG signals are prone to artifacts due to eye movement, muscle contraction, and electrical noise. Mitigating these artifacts is important for an accurate vigilance level assessment. Independent Component Analysis (ICA) is an effective method and has been extensively used in the suppression of EEG artifacts. However, in vigilance assessment applications, it was found to suffer from leakage of the cerebral activity into artifacts. In this work, we show that the wavelet ICA (wICA) method provides an alternative for artifact reduction, leading to improved vigilance level assessment results. We conducted an experiment in nine human subjects to induce two vigilance states, alert and vigilance decrement, while performing a Stroop Color–Word Test for approximately 45 min. We then compared the performance of the ICA and wICA preprocessing methods using five classifiers. Our classification results showed that in terms of features extraction, the wICA method outperformed the existing ICA method. In the delta, theta, and alpha bands, we obtained a mean classification accuracy of 84.66% using the ICA method, whereas the mean accuracy using the wICA methodwas 96.9%. However, no significant improvement was observed in the beta band. In addition, we compared the topographical map to show the changes in power spectral density across the brain regions for the two vigilance states. The proposed method showed that the frontal and central regions were most sensitive to vigilance decrement. However, in this application, the proposed wICA shows a marginal improvement compared to the Fast-ICA.American University of SharjahMDPI2025-12-17T10:46:41Z2025-12-17T10:46:41Z2022-04-15Published versionPeer-Reviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfAbu Farha, N., Al-Shargie, F., Tariq, U., & Al-Nashash, H. (2022). Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis. Sensors, 22(8), 3051. https://doi.org/10.3390/s220830511424-8220https://hdl.handle.net/11073/3256610.3390/s22083051enhttps://doi.org/10.3390/s22083051Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/oai:repository.aus.edu:11073/325662025-12-17T11:29:25Z |
| spellingShingle | Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis Abu Farha, Nadia Vigilance Assessment Noise Feature Extraction Dimensionality Reduction Thresholds Wavelet Transform Independent Component Analysis |
| status_str | publishedVersion |
| title | Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis |
| title_full | Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis |
| title_fullStr | Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis |
| title_full_unstemmed | Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis |
| title_short | Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis |
| title_sort | Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis |
| topic | Vigilance Assessment Noise Feature Extraction Dimensionality Reduction Thresholds Wavelet Transform Independent Component Analysis |
| url | https://hdl.handle.net/11073/32566 |