Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis
<p dir="ltr">Physiological signal measurement and processing are increasingly becoming popular in the ambulatory setting as the hospital-centric treatment is moving towards wearable and ubiquitous monitoring. Most of the physiological signals are highly susceptible to various types o...
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2022
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| author | Md. Shafayet Hossain (16888833) |
| author2 | Mamun Bin Ibne Reaz (16875933) Muhammad E. H. Chowdhury (14150526) Sawal H. M. Ali (16896501) Ahmad Ashrif A. Bakar (16904889) Serkan Kiranyaz (3762058) Amith Khandakar (14151981) Mohammed Alhatou (14777758) Rumana Habib (16904892) |
| author2_role | author author author author author author author author |
| author_facet | Md. Shafayet Hossain (16888833) Mamun Bin Ibne Reaz (16875933) Muhammad E. H. Chowdhury (14150526) Sawal H. M. Ali (16896501) Ahmad Ashrif A. Bakar (16904889) Serkan Kiranyaz (3762058) Amith Khandakar (14151981) Mohammed Alhatou (14777758) Rumana Habib (16904892) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Shafayet Hossain (16888833) Mamun Bin Ibne Reaz (16875933) Muhammad E. H. Chowdhury (14150526) Sawal H. M. Ali (16896501) Ahmad Ashrif A. Bakar (16904889) Serkan Kiranyaz (3762058) Amith Khandakar (14151981) Mohammed Alhatou (14777758) Rumana Habib (16904892) |
| dc.date.none.fl_str_mv | 2022-03-11T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2022.3159155 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Motion_Artifacts_Correction_From_EEG_and_fNIRS_Signals_Using_Novel_Multiresolution_Analysis/24056454 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Communications engineering Electroencephalography Functional near-infrared spectroscopy Motion artifacts Principal component analysis Mathematical models Multiresolution analysis Filtering algorithms electroencephalogram (EEG) variational mode decomposition (VMD) canonical correlation analysis (CCA) |
| dc.title.none.fl_str_mv | Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Physiological signal measurement and processing are increasingly becoming popular in the ambulatory setting as the hospital-centric treatment is moving towards wearable and ubiquitous monitoring. Most of the physiological signals are highly susceptible to various types of noises, especially movement artifacts. The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals are no exception to motion artifacts, which become prominent in the ambulatory setting. Since successful detection of various neurological disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove motion artifacts from these two signal modalities using reliable and robust methods. This paper proposes three novel multiresolution analysis techniques: i) Variational mode decomposition (VMD), ii) VMD in combination with principal component analysis (VMD-PCA), and iii) VMD in combination with canonical correlation analysis (VMD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these novel techniques is validated by computing the difference in the signal to noise ratio ( ΔSNR ) and percentage reduction in motion artifacts ( η ). Among the three proposed novel methods, VMD-CCA decomposed with 15 intrinsic mode functions (IMFs) has shown the best denoising performance for EEG signals producing an average ΔSNR and η values of 23.81 dB and 57.01%, respectively for all 23 EEG recordings. On the other hand, for the available 16 fNIRS recordings, VMD-CCA decomposed with 10 IMFs produced an average ΔSNR and η values of 15.97 dB and 39.01%, respectively. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3159155" target="_blank">https://dx.doi.org/10.1109/access.2022.3159155</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_323fad89f4aeff877c98a52368094529 |
| identifier_str_mv | 10.1109/access.2022.3159155 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056454 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution AnalysisMd. Shafayet Hossain (16888833)Mamun Bin Ibne Reaz (16875933)Muhammad E. H. Chowdhury (14150526)Sawal H. M. Ali (16896501)Ahmad Ashrif A. Bakar (16904889)Serkan Kiranyaz (3762058)Amith Khandakar (14151981)Mohammed Alhatou (14777758)Rumana Habib (16904892)EngineeringBiomedical engineeringCommunications engineeringElectroencephalographyFunctional near-infrared spectroscopyMotion artifactsPrincipal component analysisMathematical modelsMultiresolution analysisFiltering algorithmselectroencephalogram (EEG)variational mode decomposition (VMD)canonical correlation analysis (CCA)<p dir="ltr">Physiological signal measurement and processing are increasingly becoming popular in the ambulatory setting as the hospital-centric treatment is moving towards wearable and ubiquitous monitoring. Most of the physiological signals are highly susceptible to various types of noises, especially movement artifacts. The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals are no exception to motion artifacts, which become prominent in the ambulatory setting. Since successful detection of various neurological disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove motion artifacts from these two signal modalities using reliable and robust methods. This paper proposes three novel multiresolution analysis techniques: i) Variational mode decomposition (VMD), ii) VMD in combination with principal component analysis (VMD-PCA), and iii) VMD in combination with canonical correlation analysis (VMD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these novel techniques is validated by computing the difference in the signal to noise ratio ( ΔSNR ) and percentage reduction in motion artifacts ( η ). Among the three proposed novel methods, VMD-CCA decomposed with 15 intrinsic mode functions (IMFs) has shown the best denoising performance for EEG signals producing an average ΔSNR and η values of 23.81 dB and 57.01%, respectively for all 23 EEG recordings. On the other hand, for the available 16 fNIRS recordings, VMD-CCA decomposed with 10 IMFs produced an average ΔSNR and η values of 15.97 dB and 39.01%, respectively. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3159155" target="_blank">https://dx.doi.org/10.1109/access.2022.3159155</a></p>2022-03-11T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2022.3159155https://figshare.com/articles/journal_contribution/Motion_Artifacts_Correction_From_EEG_and_fNIRS_Signals_Using_Novel_Multiresolution_Analysis/24056454CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240564542022-03-11T00:00:00Z |
| spellingShingle | Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis Md. Shafayet Hossain (16888833) Engineering Biomedical engineering Communications engineering Electroencephalography Functional near-infrared spectroscopy Motion artifacts Principal component analysis Mathematical models Multiresolution analysis Filtering algorithms electroencephalogram (EEG) variational mode decomposition (VMD) canonical correlation analysis (CCA) |
| status_str | publishedVersion |
| title | Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis |
| title_full | Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis |
| title_fullStr | Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis |
| title_full_unstemmed | Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis |
| title_short | Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis |
| title_sort | Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis |
| topic | Engineering Biomedical engineering Communications engineering Electroencephalography Functional near-infrared spectroscopy Motion artifacts Principal component analysis Mathematical models Multiresolution analysis Filtering algorithms electroencephalogram (EEG) variational mode decomposition (VMD) canonical correlation analysis (CCA) |