Use of EEG Workload Indices for Diagnostic Monitoring of Vigilance Decrement

Kamzanova, Altyngul; Kustubayeva, Almira; Matthews, Gerald

doi:10.1177/0018720814526617

Cited by 117 publications

(91 citation statements)

References 49 publications

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“…For descriptors, the max descriptor was the most frequent. For frequency bands, the frequencies between 4-16Hz (i.e., theta, alpha, low-beta) were more relevant to workload estimation, and these findings aligned with results reported in References [54,92]. Although our results aligned with many current studies, other studies reported relationships between EEG and workload that were not observed in the current study.…”

Section: A Closer Examination Of Eeg Featuressupporting

confidence: 91%

Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery

Zhou

Cha

Gonzalez

et al. 2020

J. Hum.-Robot Interact.

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Monitoring surgeon workload during robot-assisted surgery can guide allocation of task demands, adapt system interfaces, and assess the robotic system's usability. Current practices for measuring cognitive load primarily rely on questionnaires that are subjective and disrupt surgical workflow. To address this limitation, a computational framework is demonstrated to predict user workload during telerobotic surgery. This framework leverages wireless sensors to monitor surgeons' cognitive load and predict their cognitive states. Continuous data across multiple physiological modalities (e.g., heart rate variability, electrodermal, and electroencephalogram activity) were simultaneously recorded for twelve surgeons performing surgical skills tasks on the validated da Vinci Skills Simulator. These surgical tasks varied in difficulty levels, e.g., requiring varying visual processing demand and degree of fine motor control. Collected multimodal physiological signals were fused using independent component analysis, and the predicted results were compared to the ground-truth workload level. Results compared performance of different classifiers, sensor fusion schemes, and physiological modality (i.e., prediction with single vs. multiple modalities). It was found that our multisensor approach outperformed individual signals and can correctly predict cognitive workload levels 83.2% of the time during basic and complex surgical skills tasks. CCS Concepts: • Human-centered computing → Human computer interaction (HCI); Interactive systems and tools; User interface management systems;

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Section: A Closer Examination Of Eeg Featuressupporting

confidence: 91%

Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery

Zhou

Cha

Gonzalez

et al. 2020

J. Hum.-Robot Interact.

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“…This processing generated an increase in mental activity, and a high perceptual process load can impair the ability to detect stimuli in environments that overload the working memory 24 . It might worsen attention and concentration, resulting in a decrease in vigilance and an increase in mental effort when associated with another low frequency, such as alpha 25 , and this may partly explain our findings. Another important finding was that we did not find a significant difference between the rest conditions and meditation at the prefrontal derivations (Fp1, Fp2) in the MG.…”

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confidence: 76%

Lower trait frontal theta activity in mindfulness meditators

Tanaka

Peressutti

Teixeira

et al. 2014

Arq. Neuro-Psiquiatr.

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Acute and long-term effects of mindfulness meditation on theta-band activity are not clear. The aim of this study was to investigate frontal theta differences between long- and short-term mindfulness practitioners before, during, and after mindfulness meditation. Twenty participants were recruited, of which 10 were experienced Buddhist meditators. Despite an acute increase in the theta activity during meditation in both the groups, the meditators showed lower trait frontal theta activity. Therefore, we suggested that this finding is a neural correlate of the expert practitioners’ ability to limit the processing of unnecessary information (e.g., discursive thought) and increase the awareness of the essential content of the present experience. In conclusion, acute changes in the theta band throughout meditation did not appear to be a specific correlate of mindfulness but were rather related to the concentration properties of the meditation. Notwithstanding, lower frontal theta activity appeared to be a trait of mindfulness practices.

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“…Current vigilance research suggests that hemodynamic indices of frontal brain metabolism such as CBFV (Warm et al, 2012) and EEG measures (Kamzanova, Kustubayeva, & Matthews, 2014) may be more diagnostic of vigilance than traditional autononic arousal measures.…”

Section: Discussionmentioning

confidence: 99%