Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation
This paper presents a novel method for classifying four different levels of cognitive workload. The workload levels are generated using visual stimuli degradation. EEG signals recorded from 16 subjects were used for workload classification. The proposed solution includes preprocessing of EEG signals...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| التنسيق: | article |
| منشور في: |
2017
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/8896 |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513442427174912 |
|---|---|
| author | Mahmoud, Rwan Adil Osman |
| author2 | Shanableh, Tamer Bodala, Indu P. Thakor, Nitish V. Al-Nashash, Hasan |
| author2_role | author author author author |
| author_facet | Mahmoud, Rwan Adil Osman Shanableh, Tamer Bodala, Indu P. Thakor, Nitish V. Al-Nashash, Hasan |
| author_role | author |
| dc.creator.none.fl_str_mv | Mahmoud, Rwan Adil Osman Shanableh, Tamer Bodala, Indu P. Thakor, Nitish V. Al-Nashash, Hasan |
| dc.date.none.fl_str_mv | 2017-08-16T07:49:18Z 2017-08-16T07:49:18Z 2017-07 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Mahmoud, R., Shanableh, T., Bodala, I., Thakor, N., & Al-Nashash, H. (2017). Novel classification system for classifying cognitive workload levels under vague visual stimulation. IEEE Sensors Journal, doi:10.1109/JSEN.2017.2727539 1530-437X http://hdl.handle.net/11073/8896 10.1109/JSEN.2017.2727539 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | http://doi.org/10.1109/JSEN.2017.2727539 |
| dc.subject.none.fl_str_mv | Channel selection Cognitive workload Electroencephalogram (EEG) Stepwise regression |
| dc.title.none.fl_str_mv | Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation |
| dc.type.none.fl_str_mv | Postprint Peer-Reviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This paper presents a novel method for classifying four different levels of cognitive workload. The workload levels are generated using visual stimuli degradation. EEG signals recorded from 16 subjects were used for workload classification. The proposed solution includes preprocessing of EEG signals and feature extraction based on statistical features. This is followed by variable selection using stepwise regression and multiclass linear classification. The presented method achieved an average classification accuracy of 93.4%. The effect of EEG channel selection on the classification accuracy is also investigated. In comparison to the existing work, we show that the proposed solution is more accurate and computationally less demanding. |
| format | article |
| id | aus_2a6c7686f8a0d5d901078094cc4e8c10 |
| identifier_str_mv | Mahmoud, R., Shanableh, T., Bodala, I., Thakor, N., & Al-Nashash, H. (2017). Novel classification system for classifying cognitive workload levels under vague visual stimulation. IEEE Sensors Journal, doi:10.1109/JSEN.2017.2727539 1530-437X 10.1109/JSEN.2017.2727539 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/8896 |
| publishDate | 2017 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual StimulationMahmoud, Rwan Adil OsmanShanableh, TamerBodala, Indu P.Thakor, Nitish V.Al-Nashash, HasanChannel selectionCognitive workloadElectroencephalogram (EEG)Stepwise regressionThis paper presents a novel method for classifying four different levels of cognitive workload. The workload levels are generated using visual stimuli degradation. EEG signals recorded from 16 subjects were used for workload classification. The proposed solution includes preprocessing of EEG signals and feature extraction based on statistical features. This is followed by variable selection using stepwise regression and multiclass linear classification. The presented method achieved an average classification accuracy of 93.4%. The effect of EEG channel selection on the classification accuracy is also investigated. In comparison to the existing work, we show that the proposed solution is more accurate and computationally less demanding.IEEE2017-08-16T07:49:18Z2017-08-16T07:49:18Z2017-07PostprintPeer-Reviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMahmoud, R., Shanableh, T., Bodala, I., Thakor, N., & Al-Nashash, H. (2017). Novel classification system for classifying cognitive workload levels under vague visual stimulation. IEEE Sensors Journal, doi:10.1109/JSEN.2017.27275391530-437Xhttp://hdl.handle.net/11073/889610.1109/JSEN.2017.2727539en_UShttp://doi.org/10.1109/JSEN.2017.2727539oai:repository.aus.edu:11073/88962024-08-22T12:08:29Z |
| spellingShingle | Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation Mahmoud, Rwan Adil Osman Channel selection Cognitive workload Electroencephalogram (EEG) Stepwise regression |
| status_str | publishedVersion |
| title | Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation |
| title_full | Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation |
| title_fullStr | Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation |
| title_full_unstemmed | Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation |
| title_short | Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation |
| title_sort | Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation |
| topic | Channel selection Cognitive workload Electroencephalogram (EEG) Stepwise regression |
| url | http://hdl.handle.net/11073/8896 |