Classification of Cognitive Workload Levels under Vague Visual Stimulation
A Master of Science thesis in Computer Engineering by Rwan Adil Osman Mahmoud entitled, "Classification of Cognitive Workload Levels under Vague Visual Stimulation," submitted in May 2016. Thesis advisor is Dr. Tamer Shanableh and thesis co-advisor is Dr. Hasan Al Nashash. Soft and hard co...
Saved in:
| Main Author: | |
|---|---|
| Format: | doctoralThesis |
| Published: |
2016
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/11073/8324 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513437922492416 |
|---|---|
| author | Mahmoud, Rwan Adil Osman |
| author_facet | Mahmoud, Rwan Adil Osman |
| author_role | author |
| dc.contributor.none.fl_str_mv | Shanableh, Tamer Al Nashash, Hasan |
| dc.creator.none.fl_str_mv | Mahmoud, Rwan Adil Osman |
| dc.date.none.fl_str_mv | 2016-06-05T07:07:34Z 2016-06-05T07:07:34Z 2016-05 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2016.16 http://hdl.handle.net/11073/8324 |
| dc.language.none.fl_str_mv | en_US |
| dc.subject.none.fl_str_mv | Cognitive workload EEG Electroencephalogram (EEG) DWT Discrete wavelet transform (DWT) stepwise regression channel selection Electroencephalography Data processing Cognition Classification |
| dc.title.none.fl_str_mv | Classification of Cognitive Workload Levels under Vague Visual Stimulation |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Computer Engineering by Rwan Adil Osman Mahmoud entitled, "Classification of Cognitive Workload Levels under Vague Visual Stimulation," submitted in May 2016. Thesis advisor is Dr. Tamer Shanableh and thesis co-advisor is Dr. Hasan Al Nashash. Soft and hard copy available. |
| format | doctoralThesis |
| id | aus_159efad33e056cb02573bec8b82708a8 |
| identifier_str_mv | 35.232-2016.16 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/8324 |
| publishDate | 2016 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Classification of Cognitive Workload Levels under Vague Visual StimulationMahmoud, Rwan Adil OsmanCognitive workloadEEGElectroencephalogram (EEG)DWTDiscrete wavelet transform (DWT)stepwise regressionchannel selectionElectroencephalographyData processingCognitionClassificationA Master of Science thesis in Computer Engineering by Rwan Adil Osman Mahmoud entitled, "Classification of Cognitive Workload Levels under Vague Visual Stimulation," submitted in May 2016. Thesis advisor is Dr. Tamer Shanableh and thesis co-advisor is Dr. Hasan Al Nashash. Soft and hard copy available.In most applications where humans are involved, it is important to augment the interaction between users and the components of these applications. One significant element is the cognitive state of the subjects involved. The cognitive state can be manipulated by the amount of cognitive workload allocated to the working memory. If the assigned cognitive workload is too low, the subject's cognition will be underutilized. In contrast, if the workload is more than the subject's capabilities, he or she will be mentally overloaded. Thus, there is a serious need to accurately assess and quantify cognitive workload levels.In this work, a method for separating four different cognitive workload levels is presented. We use an existing data set that contains EEG signals recorded from sixteen subjects while experiencing four different levels of cognitive workload. Some of these workload levels is due to the degradation of visual stimuli. The proposed solution integrates preprocessing of EEG signals, feature extraction based on discrete wavelet transform and statistical features, dimensionality reduction using stepwise regression and multiclass linear classification. Experimental results show that the average classification accuracy of the presented method is 93.4%. The effect of EEG channel selection on the classification accuracy is also investigated. The results show that channels included in the brain frontal lobes are important in cognitive workload classification. By utilizing only 23 channels, most of them are located in the frontal region; the proposed solution provides an average classification accuracy of 91%. It is shown that the proposed solution is more accurate and computationally less demanding when compared to the existing work.College of EngineeringDepartment of Computer Science and EngineeringMaster of Science in Computer Engineering (MSCoE)Shanableh, TamerAl Nashash, Hasan2016-06-05T07:07:34Z2016-06-05T07:07:34Z2016-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2016.16http://hdl.handle.net/11073/8324en_USoai:repository.aus.edu:11073/83242025-06-26T12:16:50Z |
| spellingShingle | Classification of Cognitive Workload Levels under Vague Visual Stimulation Mahmoud, Rwan Adil Osman Cognitive workload EEG Electroencephalogram (EEG) DWT Discrete wavelet transform (DWT) stepwise regression channel selection Electroencephalography Data processing Cognition Classification |
| status_str | publishedVersion |
| title | Classification of Cognitive Workload Levels under Vague Visual Stimulation |
| title_full | Classification of Cognitive Workload Levels under Vague Visual Stimulation |
| title_fullStr | Classification of Cognitive Workload Levels under Vague Visual Stimulation |
| title_full_unstemmed | Classification of Cognitive Workload Levels under Vague Visual Stimulation |
| title_short | Classification of Cognitive Workload Levels under Vague Visual Stimulation |
| title_sort | Classification of Cognitive Workload Levels under Vague Visual Stimulation |
| topic | Cognitive workload EEG Electroencephalogram (EEG) DWT Discrete wavelet transform (DWT) stepwise regression channel selection Electroencephalography Data processing Cognition Classification |
| url | http://hdl.handle.net/11073/8324 |