EEG Coherence Metrics for Vigilance: Sensitivity to Workload, Time-on-Task, and Individual Differences

Kamzanova, Altyngul; Matthews, Gerald; Kustubayeva, Almira

doi:10.1007/s10484-020-09461-4

Cited by 11 publications

(10 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, one model that is gaining influence in the understanding of the human brain is the “communication through coherence” model (Fries, 2005, 2015), which suggests that neural synchronization is the functional mechanism by which information transmission and perceptual binding occurs in the brain (Chapeton et al, 2019; de Vries et al, 2020). We suggest that the systematic study of the effect of CWL on the interareal coherence and functional connectivity of the brain could be of interest to complete our understanding of the effect of CWL on our brain activity (e.g., Kamzanova et al, 2020; Muthukrishnan et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

EEGpower spectral measures of cognitive workload: A meta‐analysis

2022

View full text Add to dashboard Cite

Cognitive workload (CWL) is a fundamental concept in the assessment and monitoring of human performance during cognitive tasks. Numerous studies have attempted to objectively and continuously measure the CWL using neuroimaging techniques. Although the electroencephalogram (EEG) is a widely used technique, the impact of CWL on the spectral power of brain frequencies has shown inconsistent results. The present review aimed to synthesize the results of the literature and quantitatively assess which brain frequency is the most sensitive to CWL. A systematic literature search following PRISMA recommendations highlighted three main frequency bands used to measure CWL: theta (4–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz). Three meta‐analyses were conducted to quantitatively examine the effect of CWL on these frequencies. A total of 45 effect sizes from 24 studies involving 723 participants were computed. CWL was associated with significant effects on theta (g = 0.68, CI [0.41, 0.95]), alpha (g = −0.25, CI [−0.45, 0.04]), and beta (g = 0.50, CI [0.21, 0.79]) power. Our results suggests that theta, especially the frontal theta, is the best index of CWL. Alpha and beta power were also significantly impacted by CWL; however, their association seemed less straightforward. These results are critically analyzed considering the literature on cerebral oscillations. We conclude by emphasizing the need to investigate the interaction between CWL and other factors that may influence spectral power (e.g., emotional load), and to combine this measure with other methods of analysis of the central and peripheral nervous system (e.g., functional connectivity, heart rate).

show abstract

Section: Discussionmentioning

confidence: 99%

EEGpower spectral measures of cognitive workload: A meta‐analysis

2022

View full text Add to dashboard Cite

show abstract

“…Maintaining a common level of theta-diapason with a correct solution, apart from the task, along with desynchronization in the alpha-diapason, might indicate the activation of the long-term memory system and a common level of the working memory load [27,28]. The reduction of the theta-diapason emerging can be associated with a decrease in cognitive control and difficulty focusing on the task [20,[29][30][31]. With the solution is wrong more pronounced desynchronization of alpha-frequencies indicates a greater load on long-term memory.…”

Section: Discussionmentioning

confidence: 99%

“…Digitalized EEG, as well as the stimuli and button press markers, were exported into the MATLAB for further processing. The time required to solve individual tasks of each type, duration of each stage, the number of terms used, and the values of spectral power of four EEG diapasons were analyzed: delta (1-3.5 Hz), theta (4-8 Hz) alpha (8-13 Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30).…”

Section: Fig 1 Stages Of a Fraction Task Solvingmentioning

confidence: 99%

Psychophysiological features of solving mathematical examples with fractions as adaptability markers to the cognitive load

Fomina

Ganusha

2021

E3S Web Conf.

View full text Add to dashboard Cite

This study is devoted to identifying a solution algorithm for standard fractions as one of the tasks that allow investigating the level of human adaptability to the cognitive load. The influential factor for a successful solution was the number of stages, and for an unsuccessful one - their duration. It was revealed that the solution success/failure correlated with the spectral power values and ratio in the theta- and alpha-diapasons of the EEG. The successful solution is accompanied by the maintenance of a stable level of theta-diapason and desynchronization in the alpha-diapason. The unsuccessful solution is characterized by an increase in the theta-diapason power, its shift to the frontal zones, and a lack of alpha-desynchronization.

show abstract

“…Changes in EEG activity have been shown to provide a reliable index of performance during visual attention tasks [7,8,9,13,14,16,20,25,29]. EEG has also been performed on other types of attention including focused [10,15] , spatial [11,31,34,35], selective [12,26,27] , auditory [25,30], search [5,6,22,28], target discrimination [17,36] , continuous [18,21], covert [32,33], sustained [23], and memory tasks [19]. Due to the variable nature of electrical activity in EEG, wavelength frequencies (Hz) have been classified as the following:…”

Section: Eeg and The Metrics Of Attentionmentioning

confidence: 99%

EEG Band Patterns for Top-Down vs Bottom-Up Control During the Psychomotor Vigilance Task: A Meta-Analysis

Meszaros

2024

Preprint

View full text Add to dashboard Cite

Monitoring cognitive vigilance during attentive tasks, such as the psychomotor vigilance test (PVT), helps evaluate performance. EEG can be used to track the type of attentive control being used during task progression: top-down (TD; goal-driven) or bottom-up (BU; salient driven). This is contingent on the dichotomous view of attention (Gaspelin & Luck, 2018), which excludes a third proposed mechanism: selection bias (Awh et al., 2012). In this literature review, it is proposed that low frequency beta (β) waves are a strong index of TD control. BU control, however, lacks a clear EEG metric such as proposed gamma (𝛾) waves . Additionally, mission constraints prevent the ideal use of both spatial fMRI and temporal EEG. For such a metric, a follow up investigation measuring changes in β wave activity from baseline may provide an indirect metric of BU control. Furthermore, a general lack of understanding of BU control and its possible task subsets warrants exploration. Keywords: psychomotor vigilance task (PVT), electroencephalogram (EEG), top-down control, bottom-up control, beta (β) waves, gamma (𝛾) waves Literature Review The Mechanisms of AttentionThe conventional view of attention has been viewed as a dichotomous mechanism: top-down control or bottom-up control. Top-down control (TD) is simply defined as the goal-driven type of attention including both volition and stimuli context (i.e. trying to stay focused while reading in a noisy library). In contrast, bottom-up control (BU) can be simply defined as salient driven or distraction driven attention (i.e. shifting attention to a loud noise instead of reading the book) [1]. TD and BU control is also commonly associated with feedforward (BU) and feedback (TD) descriptions of neural activity [35]. While the terminology of BU control is generally agreed upon, the scope of TD control is debated and not universally agreed upon. Proposals of a third category of attention involve "selection bias": the reward history and selection history of a specific stimuli [2,3]. It was argued that context of the stimuli, while internal by nature, should not be classified as TD control since volition and context are vastly different [2]. The mechanisms of these two components (volition vs context) are inherently different: selection context "is fast, automatic and occurs without much, if any, effort" ([3], p. 29). It was thus proposed that most selection is conducted via selection bias, therefore reducing the frequency of use of TD control [3]. In contrast, the conventional and current AFRL view of TD control can be summarized as "'top-down' processing to refer to any perceptual phenomenon that is influenced by context, learning, or expectation, which would include selection history" ([1], p. 25). This is not to say that current AFRL cognitive models, such as ACT-R, cannot comport with "selection bias" as the mechanics of cognition are present. It is important to note these are terminology disagreements (no experimentation has taken place).

show abstract

EEG Coherence Metrics for Vigilance: Sensitivity to Workload, Time-on-Task, and Individual Differences

Cited by 11 publications

References 53 publications

EEGpower spectral measures of cognitive workload: A meta‐analysis

EEGpower spectral measures of cognitive workload: A meta‐analysis

Psychophysiological features of solving mathematical examples with fractions as adaptability markers to the cognitive load

EEG Band Patterns for Top-Down vs Bottom-Up Control During the Psychomotor Vigilance Task: A Meta-Analysis

Contact Info

Product

Resources

About