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The vigilance decrement in performance is a significant operational issue in various applied settings. Psychophysiological methods for diagnostic monitoring of vigilance have focused on power spectral density measures from the electroencephalogram (EEG). This article addresses the diagnosticity of an alternative set of EEG measures, coherence between different electrode sites. Coherence metrics may index the functional connectivity between brain regions that supports sustained attention. Coherence was calculated for seven pre-defined brain networks. Workload and time-on-task factors primarily influenced alpha and theta coherence in anterior, central, and inter-hemispheric networks. Individual differences in coherence in inter-hemispheric, left intro-hemispheric and posterior networks correlated with performance. These findings demonstrate the potential applied utility of coherence metrics, although several methodological limitations and challenges must be overcome. [ABSTRACT FROM AUTHOR]

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 (El). 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 lower-frequency alpha. Surprisingly, higher TLI was associated with superior performance. Frontal theta and El 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Depressed patients are characterized by hypoactivity of the left and hyperactivity of the right frontal areas during the resting state. Depression is also associated with impaired decision-making, which reflects multiple cognitive, affective, and attentional processes, some of which may be lateralized. The aim of this study was to investigate brain asymmetry during a decision-making task performed in negative and positive feedback conditions in patients with Major Depressive Disorder (MDD) in comparison to healthy control participants. The electroencephalogram (EEG) was recorded from 60 MDD patients and 60 healthy participants while performing a multi-stage decision-making task. Frontal, central, and parietal alpha asymmetry were analyzed with EEGlab/ERPlab software. Evoked potential responses (ERPs) showed general lateralization suggestive of an initial right dominance developing into a more complex pattern of asymmetry across different scalp areas as information was processed. The MDD group showed impaired mood prior to performance, and decreased confidence during performance in comparison to the control group. The resting state frontal alpha asymmetry showed lateralization in the healthy group only. Task-induced alpha power and ERP P100 and P300 amplitudes were more informative biomarkers of depression during decision making. Asymmetry coefficients based on task alpha power and ERP amplitudes showed consistency in the dynamical changes during the decision-making stages. Depression was characterized by a lack of left dominance during the resting state and left hypoactivity during the task baseline and subsequent decision-making process. Findings add to understanding of the functional significance of lateralized brain processes in depression. [ABSTRACT FROM AUTHOR]