EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis
This paper proposes two novel methods to classify semantic vigilance levels by utilizing EEG directed connectivity patterns with their corresponding graphical network measures. We estimate the directed connectivity using relative wavelet transform entropy (RWTE) and partial directed coherence (PDC)...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2020
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/21414 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513440703315968 |
|---|---|
| author | Yahya, Fares |
| author2 | Hassanin, Omnia Tariq, Usman Al-Nashash, Hasan |
| author2_role | author author author |
| author_facet | Yahya, Fares Hassanin, Omnia Tariq, Usman Al-Nashash, Hasan |
| author_role | author |
| dc.creator.none.fl_str_mv | Yahya, Fares Hassanin, Omnia Tariq, Usman Al-Nashash, Hasan |
| dc.date.none.fl_str_mv | 2020 2021-04-15T09:29:57Z 2021-04-15T09:29:57Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | F. M. Al-Shargie, O. Hassanin, U. Tariq and H. Al-Nashash, "EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis," in IEEE Access, vol. 8, pp. 115941-115956, 2020, doi: 10.1109/ACCESS.2020.3004504. 2169-3536 http://hdl.handle.net/11073/21414 10.1109/ACCESS.2020.3004504 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://doi.org/10.1109/ACCESS.2020.3004504 |
| dc.subject.none.fl_str_mv | Vigilance decrement Electroencephalogram Relative wavelet transform entropy Partial directed coherence Graph theory analysis Machine Learning |
| dc.title.none.fl_str_mv | EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This paper proposes two novel methods to classify semantic vigilance levels by utilizing EEG directed connectivity patterns with their corresponding graphical network measures. We estimate the directed connectivity using relative wavelet transform entropy (RWTE) and partial directed coherence (PDC) and the graphical network measures by graph theory analysis (GTA) at four frequency bands. The RWTE and PDC quantify the strength and directionality of information flow between EEG nodes. On the other hand, the GTA of the complex network measures summarizes the topological structure of the network. We then evaluate the proposed methods using machine learning classifiers. We carried out an experiment on nine subjects performing semantic vigilance task (Stroop color word test (SCWT)) for approximately 45 minutes. Behaviorally, all subjects demonstrated vigilance decrement as reflected by the significant increase in response time and reduced accuracy. The strength and directionality of information flow in the connectivity network by RWTE/PDC and the GTA measures significantly decrease with vigilance decrement, p<0.05. The classification results show that the proposed methods outperform other related and competitive methods available in the literature and achieve 100% accuracy in subject-dependent and above 89% in subject-independent level in each of the four frequency bands. The overall results indicate that the proposed methods of directed connectivity patterns and GTA provide a complementary aspect of functional connectivity. Our study suggests directed functional connectivity with GTA as informative features and highlight Support Vector Machine as the suitable classifier for classifying semantic vigilance levels. |
| format | article |
| id | aus_d523359b5baf8f6db5aca7a6aa1356b9 |
| identifier_str_mv | F. M. Al-Shargie, O. Hassanin, U. Tariq and H. Al-Nashash, "EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis," in IEEE Access, vol. 8, pp. 115941-115956, 2020, doi: 10.1109/ACCESS.2020.3004504. 2169-3536 10.1109/ACCESS.2020.3004504 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/21414 |
| publishDate | 2020 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory AnalysisYahya, FaresHassanin, OmniaTariq, UsmanAl-Nashash, HasanVigilance decrementElectroencephalogramRelative wavelet transform entropyPartial directed coherenceGraph theory analysisMachine LearningThis paper proposes two novel methods to classify semantic vigilance levels by utilizing EEG directed connectivity patterns with their corresponding graphical network measures. We estimate the directed connectivity using relative wavelet transform entropy (RWTE) and partial directed coherence (PDC) and the graphical network measures by graph theory analysis (GTA) at four frequency bands. The RWTE and PDC quantify the strength and directionality of information flow between EEG nodes. On the other hand, the GTA of the complex network measures summarizes the topological structure of the network. We then evaluate the proposed methods using machine learning classifiers. We carried out an experiment on nine subjects performing semantic vigilance task (Stroop color word test (SCWT)) for approximately 45 minutes. Behaviorally, all subjects demonstrated vigilance decrement as reflected by the significant increase in response time and reduced accuracy. The strength and directionality of information flow in the connectivity network by RWTE/PDC and the GTA measures significantly decrease with vigilance decrement, p<0.05. The classification results show that the proposed methods outperform other related and competitive methods available in the literature and achieve 100% accuracy in subject-dependent and above 89% in subject-independent level in each of the four frequency bands. The overall results indicate that the proposed methods of directed connectivity patterns and GTA provide a complementary aspect of functional connectivity. Our study suggests directed functional connectivity with GTA as informative features and highlight Support Vector Machine as the suitable classifier for classifying semantic vigilance levels.American University of SharjahIEEE2021-04-15T09:29:57Z2021-04-15T09:29:57Z2020Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfF. M. Al-Shargie, O. Hassanin, U. Tariq and H. Al-Nashash, "EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis," in IEEE Access, vol. 8, pp. 115941-115956, 2020, doi: 10.1109/ACCESS.2020.3004504.2169-3536http://hdl.handle.net/11073/2141410.1109/ACCESS.2020.3004504en_UShttps://doi.org/10.1109/ACCESS.2020.3004504oai:repository.aus.edu:11073/214142024-08-22T12:08:17Z |
| spellingShingle | EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis Yahya, Fares Vigilance decrement Electroencephalogram Relative wavelet transform entropy Partial directed coherence Graph theory analysis Machine Learning |
| status_str | publishedVersion |
| title | EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis |
| title_full | EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis |
| title_fullStr | EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis |
| title_full_unstemmed | EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis |
| title_short | EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis |
| title_sort | EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis |
| topic | Vigilance decrement Electroencephalogram Relative wavelet transform entropy Partial directed coherence Graph theory analysis Machine Learning |
| url | http://hdl.handle.net/11073/21414 |