Please use this identifier to cite or link to this item: https://doi.org/10.1088/1741-2560/12/4/046020
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dc.titleCognitive workload modulation through degraded visual stimuli: A single-trial EEG study
dc.contributor.authorYu, K
dc.contributor.authorPrasad, I
dc.contributor.authorMir, H
dc.contributor.authorThakor, N
dc.contributor.authorAl-Nashash, H
dc.date.accessioned2020-09-03T10:34:02Z
dc.date.available2020-09-03T10:34:02Z
dc.date.issued2015
dc.identifier.citationYu, K, Prasad, I, Mir, H, Thakor, N, Al-Nashash, H (2015). Cognitive workload modulation through degraded visual stimuli: A single-trial EEG study. Journal of Neural Engineering 12 (4) : 46020. ScholarBank@NUS Repository. https://doi.org/10.1088/1741-2560/12/4/046020
dc.identifier.issn17412560
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/174122
dc.description.abstractObjective. Our experiments explored the effect of visual stimuli degradation on cognitive workload. Approach. We investigated the subjective assessment, event-related potentials (ERPs) as well as electroencephalogram (EEG) as measures of cognitive workload. Main results. These experiments confirm that degradation of visual stimuli increases cognitive workload as assessed by subjective NASA task load index and confirmed by the observed P300 amplitude attenuation. Furthermore, the single-trial multi-level classification using features extracted from ERPs and EEG is found to be promising. Specifically, the adopted single-trial oscillatory EEG/ERP detection method achieved an average accuracy of 85% for discriminating 4 workload levels. Additionally, we found from the spatial patterns obtained from EEG signals that the frontal parts carry information that can be used for differentiating workload levels. Significance. Our results show that visual stimuli can modulate cognitive workload, and the modulation can be measured by the single trial EEG/ERP detection method. © 2015 IOP Publishing Ltd.
dc.sourceUnpaywall 20200831
dc.subjectModulation
dc.subjectNASA
dc.subjectCognitive workloads
dc.subjectDetection methods
dc.subjectElectro-encephalogram (EEG)
dc.subjectEventrelated potential (ERPs)
dc.subjectMulti-level classifications
dc.subjectSingle trial
dc.subjectSubjective assessments
dc.subjectWork loads
dc.subjectElectroencephalography
dc.subjectArticle
dc.subjectcognition
dc.subjectdegradation
dc.subjectelectroencephalogram
dc.subjectevent related potential
dc.subjecthuman
dc.subjecthuman experiment
dc.subjectnormal human
dc.subjectpriority journal
dc.subjectspatiotemporal analysis
dc.subjectvisual stimulation
dc.subjectworkload
dc.subjectadaptation
dc.subjectassociation
dc.subjectcognition
dc.subjectelectroencephalography
dc.subjectexecutive function
dc.subjectphotostimulation
dc.subjectphysiology
dc.subjectprocedures
dc.subjectpsychomotor performance
dc.subjectvision
dc.subjectAdaptation, Physiological
dc.subjectCognition
dc.subjectCues
dc.subjectElectroencephalography
dc.subjectEvent-Related Potentials, P300
dc.subjectExecutive Function
dc.subjectHumans
dc.subjectPhotic Stimulation
dc.subjectPsychomotor Performance
dc.subjectVisual Perception
dc.typeArticle
dc.contributor.departmentDEPARTMENT OF COMPUTER SCIENCE
dc.contributor.departmentELECTRICAL AND COMPUTER ENGINEERING
dc.description.doi10.1088/1741-2560/12/4/046020
dc.description.sourcetitleJournal of Neural Engineering
dc.description.volume12
dc.description.issue4
dc.description.page46020
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