Dynamic assessment of control room operators cognitive workload using electroencephalography (EEG)

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dc.contributor.author Iqbal, M. Umair
dc.contributor.author Srinivasan, Babji
dc.contributor.author Srinivasan, Rajagopalan
dc.date.accessioned 2020-07-25T16:20:37Z
dc.date.available 2020-07-25T16:20:37Z
dc.date.issued 2020-10
dc.identifier.citation Iqbal, M. Umair; Srinivasan, Babji and Srinivasan, Rajagopalan, "Dynamic assessment of control room operators cognitive workload using electroencephalography (EEG)", Computers and Chemical Engineering, DOI: 10.1016/j.compchemeng.2020.106726, vol. 141, Oct. 2020. en_US
dc.identifier.issn 0098-1354
dc.identifier.issn 1873-4375
dc.identifier.uri http://dx.doi.org/10.1016/j.compchemeng.2020.106726
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/5577
dc.description.abstract In modern plants with high levels of automation, acquiring an adequate mental model of the process has become a challenge for operators. Studies indicate that sub-optimal decisions occur when there is a mismatch between the demands of the process and the human's capability. This mismatch leads to high cognitive workload in human operators, often a precursor for poor performance. Recently, researchers in various safety critical domains (aviation, driving, marine, NPP, etc.) have started to explore the use of physiological measurements from humans to understand their cognitive workload and its effect. In this work, we evaluate the potential of EEG to measure cognitive workload of human operators in chemical process control room. We propose a single dry electrode EEG based methodology for identifying the similarities and mismatch between the operators’ mental model of the process and the actual process behaviour during abnormal situations. Our results reveal that SƟ(ω), the power spectral density of theta (ɵ) waves (frequency range 4–7 Hz) in the EEG signal has the potential to identify such mismatches. Results indicate that SƟ(ω) is positively correlated with workload and hence can be used for assessing the cognitive workload of operators in process industries.
dc.description.statementofresponsibility by Mohd Umair Iqbal, Babji Srinivasan and Rajagopalan Srinivasan
dc.format.extent vol. 141
dc.language.iso en_US en_US
dc.publisher Elsevier en_US
dc.subject Process safety en_US
dc.subject Human error en_US
dc.subject Cognitive workload en_US
dc.subject EEG en_US
dc.subject Theta power spectral density en_US
dc.title Dynamic assessment of control room operators cognitive workload using electroencephalography (EEG) en_US
dc.type Article en_US
dc.relation.journal Computers and Chemical Engineering


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