Prediction of Response Time and Vigilance Score in a Sustained Attention Task from Pre-trial Phase Synchrony using Deep Neural Networks

Torkamani-Azar, Mastaneh; Kanik, Sümeyra Demir; Ahmed, Sara Atito Ali; Aydın, Serap; Çetin, Müjdat

doi:10.1109/embc.2019.8856291

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…It was suggested that lower parietal alpha demonstrates attentional demands while temporal and parietal beta activities show more differential hemispheric activities during emotional and cognitive processes, respectively [37]. The heat maps demonstrate that lower beta-1 band (12-16 Hz) is indeed more similar to the lower frequencies in being associated with slower responses while lower beta-2 and mid-beta bands (16)(17)(18)(19)(20)(21)(22)(23)(24) Hz) are correlates of improved and consistent performance as well as faster RT in Go trials. We also observed that higher levels of lower-beta (12-20 Hz) from parieto-occipital channels during the EO recordings were correlated with more omission errors.…”

Section: Opposite Roles Of Beta Sub-bands In Predicting Task Consimentioning

confidence: 93%

“…Next, the hit response time (HRT) is defined as the response time for correct Go trials, i.e., trials with non-target digits for which a correct click was detected. Since variations in response time indicate the inability to maintain vigilance during long attention tasks and tests of attention deficits, variability of the overall HRT was calculated as the ratio of the standard deviation to the averaged HRT [23].…”

Section: Eeg Analysis and Feature Extractionmentioning

confidence: 99%

“…Finally, we develop multivariate regression models using a thorough feature relevance analysis, and demonstrate how small feature subsets from pre-task, intrinsic networks are successful in predicting the overall task-related performance measures. These experimental setup and vigilance scores were previously used in a substantially different work that focused on predicting the performance measures from phase synchrony features obtained during the 8-minute long blocks of the actual sustained attention task [23].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Reaction Time and Vigilance Variability From Spatio-Spectral Features of Resting-State EEG in a Long Sustained Attention Task

Torkamani-Azar

Kanik

Aydın

et al. 2020

IEEE J. Biomed. Health Inform.

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Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks. However, no study has yet presented concise predictors of task-induced vigilance variability from spectrospatial features of the pre-task, resting-state electroencephalograms (EEG). We asked ten healthy volunteers (6 females, 4 males) aged from 22 to 45.5 to participate in 105-minute fixed-sequence-varying-duration sessions of sustained attention to response task (SART). A novel and adaptive vigilance scoring scheme was designed based on the performance and response time in consecutive trials, and demonstrated large inter-participant variability in terms of maintaining consistent tonic performance. Multiple linear regression using feature relevance analysis obtained significant predictors of the mean cumulative vigilance score (CVS), mean response time, and variabilities of these scores from the resting-state, bandpower ratios of EEG signals, p<0. Brodmanns areas 35 and 36). Higher ratios of parietal alpha (8-12 Hz) from the Brodmann's areas 18, 19, and 37 during the eyes-open states predicted slower responses but more consistent CVS and reactions associated with the superior ability in vigilance maintenance. The proposed framework and these first findings on the most stable and significant attention predictors from the intrinsic EEG power ratios can be used to model attention variations during the calibration sessions of BCI applications and vigilance monitoring systems. Single-layer neural networks trained with cross-validation also captured different associations for the beta sub-bands. Increase in the gamma (28-48 Hz) and upper beta (24-28 Hz) ratios from the left central and temporal regions predicted slower reactions and more inconsistent vigilance as explained by the increased activation of default mode network (DMN) and differences between the high-and low-attention networks at temporal regions (

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Section: Opposite Roles Of Beta Sub-bands In Predicting Task Consimentioning

confidence: 93%

Section: Eeg Analysis and Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Reaction Time and Vigilance Variability From Spatio-Spectral Features of Resting-State EEG in a Long Sustained Attention Task

Torkamani-Azar

Kanik

Aydın

et al. 2020

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, Xie et al suggested that the information derived from the alpha band networks could be used to predict vigilance level [75]. It has been shown that phase synchrony indices on connectivity network can be used to successfully predict the average hit response time (a measure indicating vigilance) based on deep neural network model (this is a machine learning method) [76]. An extended question might come up.…”

Section: Vigilancementioning

confidence: 99%

Cognitive State Analysis, Understanding, and Decoding from the Perspective of Brain Connectivity

Li¹,

Bezerianos²,

Thakor³

2020

Preprint

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Cognitive states are involving in our daily life, which motivates us to explore them and understand them by a vast variety of perspectives. Among these perspectives, brain connectivity is increasingly receiving attention in recent years. It is the right time to summarize the past achievements, serving as a cornerstone for the upcoming progress in the field. In this chapter, the definition of the cognitive state is first given and the cognitive states that are frequently investigated are then outlined. The introduction of approaches for estimating connectivity strength is followed. Subsequently, each cognitive state is separately described and the progress in cognitive state investigation is summarized, including analysis, understanding, and decoding. We concentrate on the literature ascertaining macro-scale representations of cognitive states from the perspective of brain connectivity and give an overview of achievements related to cognitive states to date, especially within the past ten years. The discussions and future prospects are stated at the end of the chapter.

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Cognitive State Analysis, Understanding, and Decoding from the Perspective of Brain Connectivity

Li¹,

Bezerianos²,

Thakor³

2021

Handbook of Neuroengineering

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Prediction of Response Time and Vigilance Score in a Sustained Attention Task from Pre-trial Phase Synchrony using Deep Neural Networks

Cited by 5 publications

References 15 publications

Prediction of Reaction Time and Vigilance Variability From Spatio-Spectral Features of Resting-State EEG in a Long Sustained Attention Task

Prediction of Reaction Time and Vigilance Variability From Spatio-Spectral Features of Resting-State EEG in a Long Sustained Attention Task

Cognitive State Analysis, Understanding, and Decoding from the Perspective of Brain Connectivity

Cognitive State Analysis, Understanding, and Decoding from the Perspective of Brain Connectivity

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