Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique
<p dir="ltr">The increasing prevalence of non-invasive, portable Electroencephalography (EEG) sensors for neuro-physiological measurements has propelled EEG-based assessments of cognitive load (CL) into the spotlight. In this study, we harnessed the capabilities of a four-channel, we...
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2024
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| _version_ | 1864513540928307200 |
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| author | Iqbal Hassan (22155274) |
| author2 | Monica Zolezzi (10115698) Hanan Khalil (8344038) Rafif Mahmood Al Saady (17632263) Shona Pedersen (2792278) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author |
| author_facet | Iqbal Hassan (22155274) Monica Zolezzi (10115698) Hanan Khalil (8344038) Rafif Mahmood Al Saady (17632263) Shona Pedersen (2792278) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Iqbal Hassan (22155274) Monica Zolezzi (10115698) Hanan Khalil (8344038) Rafif Mahmood Al Saady (17632263) Shona Pedersen (2792278) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2024-09-03T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3428691 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Cognitive_Load_Estimation_Using_a_Hybrid_Cluster-Based_Unsupervised_Machine_Learning_Technique/30023401 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Human-centred computing Machine learning Cognitive load unsupervised machine learning electroencephalography (EEG) brain-computer interface (BCI) Electroencephalography Real-time systems Feature extraction Surveys Recording Electrodes Unsupervised learning Machine learning Computer interfaces |
| dc.title.none.fl_str_mv | Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The increasing prevalence of non-invasive, portable Electroencephalography (EEG) sensors for neuro-physiological measurements has propelled EEG-based assessments of cognitive load (CL) into the spotlight. In this study, we harnessed the capabilities of a four-channel, wearable EEG device that captured brain activity data during two distinct CL states: Baseline (representing a non-CL, resting state) and the Stroop Test (a CL-inducing state). The primary objective of this study is to estimate the CL index through an innovative approach that employs a hybrid, cluster-based, unsupervised learning technique seamlessly integrated with a 1D Convolutional Neural Network (CNN) architecture tailored for automated feature extraction, rather than conventional supervised algorithms, which facilitated in the acquisition of latent complex patterns without the need for manual categorization. The approach was rigorously evaluated using stratified cross-validation, with several assessment criteria assessing both its quality and predictive capability to estimate the CL index. The results obtained (e.g., homogeneity score of 0.7, adjusted rand index of 0.78, silhouette coefficient of 0.5, and an accuracy rate of 93.2%) demonstrate that our module exhibits superiority over supervised approaches. These results are indicative that the adoption of multi-channel wearable EEG devices may facilitate real-time CL estimation, minimizing the need for extensive human intervention, and reducing potential bias, paving the way for more objective and efficient CL assessments.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" 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.2024.3428691" target="_blank">https://dx.doi.org/10.1109/access.2024.3428691</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_79421f35103da9dfae1b10662821388f |
| identifier_str_mv | 10.1109/access.2024.3428691 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30023401 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning TechniqueIqbal Hassan (22155274)Monica Zolezzi (10115698)Hanan Khalil (8344038)Rafif Mahmood Al Saady (17632263)Shona Pedersen (2792278)Muhammad E. H. Chowdhury (14150526)Biomedical and clinical sciencesNeurosciencesEngineeringBiomedical engineeringInformation and computing sciencesArtificial intelligenceHuman-centred computingMachine learningCognitive loadunsupervised machine learningelectroencephalography (EEG)brain-computer interface (BCI)ElectroencephalographyReal-time systemsFeature extractionSurveysRecordingElectrodesUnsupervised learningMachine learningComputer interfaces<p dir="ltr">The increasing prevalence of non-invasive, portable Electroencephalography (EEG) sensors for neuro-physiological measurements has propelled EEG-based assessments of cognitive load (CL) into the spotlight. In this study, we harnessed the capabilities of a four-channel, wearable EEG device that captured brain activity data during two distinct CL states: Baseline (representing a non-CL, resting state) and the Stroop Test (a CL-inducing state). The primary objective of this study is to estimate the CL index through an innovative approach that employs a hybrid, cluster-based, unsupervised learning technique seamlessly integrated with a 1D Convolutional Neural Network (CNN) architecture tailored for automated feature extraction, rather than conventional supervised algorithms, which facilitated in the acquisition of latent complex patterns without the need for manual categorization. The approach was rigorously evaluated using stratified cross-validation, with several assessment criteria assessing both its quality and predictive capability to estimate the CL index. The results obtained (e.g., homogeneity score of 0.7, adjusted rand index of 0.78, silhouette coefficient of 0.5, and an accuracy rate of 93.2%) demonstrate that our module exhibits superiority over supervised approaches. These results are indicative that the adoption of multi-channel wearable EEG devices may facilitate real-time CL estimation, minimizing the need for extensive human intervention, and reducing potential bias, paving the way for more objective and efficient CL assessments.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" 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.2024.3428691" target="_blank">https://dx.doi.org/10.1109/access.2024.3428691</a></p>2024-09-03T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3428691https://figshare.com/articles/journal_contribution/Cognitive_Load_Estimation_Using_a_Hybrid_Cluster-Based_Unsupervised_Machine_Learning_Technique/30023401CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300234012024-09-03T06:00:00Z |
| spellingShingle | Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique Iqbal Hassan (22155274) Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Human-centred computing Machine learning Cognitive load unsupervised machine learning electroencephalography (EEG) brain-computer interface (BCI) Electroencephalography Real-time systems Feature extraction Surveys Recording Electrodes Unsupervised learning Machine learning Computer interfaces |
| status_str | publishedVersion |
| title | Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique |
| title_full | Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique |
| title_fullStr | Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique |
| title_full_unstemmed | Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique |
| title_short | Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique |
| title_sort | Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique |
| topic | Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Human-centred computing Machine learning Cognitive load unsupervised machine learning electroencephalography (EEG) brain-computer interface (BCI) Electroencephalography Real-time systems Feature extraction Surveys Recording Electrodes Unsupervised learning Machine learning Computer interfaces |