Title

Use Of Eeg Workload Indices For Diagnostic Monitoring Of Vigilance Decrement

Keywords

attentional processes; mental workload; monitoring; supervisory control; vigilance (sustained attention)

Abstract

Objective: A study was run to test which of five electroencephalographic (EEG) indices was most diagnostic of loss of vigilance at two levels of workload. Background: EEG indices of alertness include conventional spectral power measures as well as indices combining measures from multiple frequency bands, such as the Task Load Index (TLI) and the Engagement Index (EI). However, it is unclear which indices are optimal for early detection of loss of vigilance. Method: Ninety-two participants were assigned to one of two experimental conditions, cued (lower workload) and uncued (higher workload), and then performed a 40-min visual vigilance task. Performance on this task is believed to be limited by attentional resource availability. EEG was recorded continuously. Performance, subjective state, and workload were also assessed. Results: The task showed a vigilance decrement in performance; cuing improved performance and reduced subjective workload. Lower-frequency alpha (8 to 10.9 Hz) and TLI were most sensitive to the task parameters. The magnitude of temporal change was larger for lowerfrequency alpha. Surprisingly, higher TLI was associated with superior performance. Frontal theta and EI were influenced by task workload only in the final period of work. Correlational data also suggested that the indices are distinct from one another. Conclusions: Lower-frequency alpha appears to be the optimal index for monitoring vigilance on the task used here, but further work is needed to test how diagnosticity of EEG indices varies with task demands. Application: Lower-frequency alpha may be used to diagnose loss of operator alertness on tasks requiring vigilance. Copyright © 2014, Human Factors and Ergonomics Society.

Publication Date

1-1-2014

Publication Title

Human Factors

Volume

56

Issue

6

Number of Pages

1136-1149

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1177/0018720814526617

Socpus ID

84906672400 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84906672400

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