Lower-frequency alpha may be used to diagnose loss of operator alertness on tasks requiring vigilance.

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.

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The resource model of vigilance (Warm, Parasuraman, & Matthews, 2008) suggests that EEG-based indices of workload might be used to monitor the operator’s fitness to sustain signal detection. 92 participants performed a 40 minute vigilance task believed to be sensitive to resource availability. Half performed in a cued condition, half without cues. Findings confirmed that cueing reduces workload and enhances vigilance. EEG was recorded throughout performance. Of the various EEG indices analyzed, lower frequency alpha and the Task Load Index (TLI) corresponded most closely to changes in signal detection rates. Other indices, the Engagement Index (EI) and frontal theta, did not show systematic decrement but discriminated cued and uncued conditions towards the end of the task. Implications of the findings for using EEG to drive adaptive automation are discussed.

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