Electroencephalography (EEG) based cognitive measures for evaluating the effectiveness of operator training

Process industries rely on effective decision-making by human operators to ensure safety. Control room operators acquire various inputs from the DCS, interpret them, make a prognosis, and respond through appropriate control actions. In order to perform these effectively, the operator needs to have a...

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Bibliographic Details
Published in:Process safety and environmental protection 2021-06, Vol.150, p.51-67
Main Authors: Iqbal, Mohd Umair, Shahab, Mohammed Aatif, Choudhary, Mahindra, Srinivasan, Babji, Srinivasan, Rajagopalan
Format: Article
Language:English
Subjects:
Clustering
Cognitive ability
Cognitive workload
Control rooms
Decision making
EEG
Electroencephalography
Mental model
Operator performance
Operators
Performance assessment
Power spectral density
Training
Workload
Workloads
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Summary:Process industries rely on effective decision-making by human operators to ensure safety. Control room operators acquire various inputs from the DCS, interpret them, make a prognosis, and respond through appropriate control actions. In order to perform these effectively, the operator needs to have appropriate mental models of the process. Poor mental models would increase the operator’s cognitive workload and make them prone to errors. Traditionally, operator training systems are used to help operators learn appropriate mental models. However, performance assessment metrics used during training do not explicitly account for their cognitive workload while performing a task. In this work, we demonstrate that this leads to an incorrect assessment of operators’ abilities. We propose an Electroencephalography (EEG) power spectral density-based metric that can quantify the cognitive workload and provide detailed insight into the evolution of the operator’s mental models during training. To demonstrate its utility, we have conducted training experiments with ten participants performing 438 tasks. Statistical studies reveal that the proposed metric can quantify the cognitive workload and therefore be used to assess operator training accurately.
ISSN:0957-5820
1744-3598
DOI:10.1016/j.psep.2021.03.050