Vigilance, alertness, or sustained attention: physiological basis and measurement

Oken, Barry; Salinsky, Martin; Elsas, Siegward-M.

doi:10.1016/j.clinph.2006.01.017

Cited by 635 publications

(441 citation statements)

References 237 publications

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“…On the group level, we found a significant increase of fatigue-related parameters in general, without being prominent at a particular site. This is in contradiction to studies which showed a prominent increase of alpha power over central and parietal sites, but not frontal sites Craig, 2001 andOken et al, 2006). However, one possible explanation is the different definition of frequency band boundaries.…”

Section: Discussioncontrasting

confidence: 51%

Assessing Drivers’ Fatigue State Under Real Traffic Conditions Using EEG Alpha Spindles

Schrauf

Simon

Schmidt

et al. 2011

Proceedings of the 6th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design : Dr

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Summary:The effectiveness of EEG alpha spindles, defined by short narrowband bursts in the alpha band, as an objective measure for assessing driver fatigue under real driving conditions was examined using an algorithm for the identification of alpha spindles. The method is applied to data recorded under real traffic conditions and compared with the performance of the traditional EEG fatigue measure alpha band power. Statistical analysis revealed significant increases from the first to the last driving section of alpha band power; with larger effect sizes for the alpha spindle based measures. An increased level of fatigue for drop-outs, as compared to participants who did not abort the drive, was observed only by means of alpha spindle parameters. EEG alpha spindle parameters increase both fatigue detection sensitivity and specificity as compared to EEG alpha band power. It is demonstrated that alpha spindles are superior to EEG band power measures for assessing driver fatigue under real traffic conditions.

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Section: Discussioncontrasting

confidence: 51%

Assessing Drivers’ Fatigue State Under Real Traffic Conditions Using EEG Alpha Spindles

Schrauf

Simon

Schmidt

et al. 2011

Proceedings of the 6th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design : Dr

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“…An LFP index of arousal was generated by taking the ratio of power in beta-and theta-range frequency bands (15-25 Hz and 3-7 Hz, respectively) at each fMRI time point, as motivated by earlier studies (e.g., as reviewed in ref. 41; see SI Methods).…”

Section: Significancementioning

confidence: 99%

Tracking brain arousal fluctuations with fMRI

Chang

Leopold

Schölvinck

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

284

288

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Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the timevarying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.resting-state fMRI | spontaneous fluctuations | arousal | electrophysiology D uring both active task engagement and rest, the human brain exhibits fluctuations in neural activity that can be readily measured using functional MRI (fMRI). In recent years, examining the spatiotemporal organization of these fluctuations has generated novel insight into the functional architecture of the human brain and its changes with development and disease (1). A prominent approach for mapping this architecture is to study interregional correlations in the fMRI signal fluctuations, which, even during rest, appear to be indicative of networks supporting specific functions. However, despite the promise and rapidly increasing application of this technique in the endeavor of brain connectomics (2-4), its sensitivity and specificity are compromised by unexplained variability arising from multiple neural and nonneural sources (e.g., refs. 5-11). As a result, the interpretation of resting-state fMRI data and the efficacy of these data as a biomarker rely critically on understanding and accounting for such sources of variability (11-13).Changes in arousal, mediated by an interaction between the ascending arousal system and the neocortex, may strongly modulate neuronal activity in much of the brain (14-18). Indeed, changes in vigilance and arousal (hereafter described jointly as "arousal"), which can be especially prevalent during the passive and uncontrolled resting state, result in fMRI signal variability that may confound the extraction of functional networks (5, 19). For example, the amplitude and extent of correlations in restingstate fMRI data vary with EEG-and behaviorally defined indicators of drowsiness and light sleep (20)(21)(22)(23)(24)(25) and are altered by sleep deprivation (26-28) and caffeine-induced changes in arousal state (29). Distinct patterns of functional connectivity across multiple networks have been associated with distinct EEG-defined sleep stages (30, 31) with sufficient reliabilit...

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“…Mental states have the characteristic of continuity. It is a gradual process, and so the EEG features extracted from mental states such as vigilance change continuously [15]. Recently, Shi and Lu [14] have applied linear dynamical system (LDS) approach to vigilance estimation [11,12,13] from EEGs, and their experimental results show that LDS can remove vigilance-unrelated signals effectively.…”

Section: Introductionmentioning

confidence: 99%

Removing Unrelated Features Based on Linear Dynamical System for Motor-Imagery-Based Brain-Computer Interface

Shi

2011

Neural Information Processing

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Abstract. Common spatial pattern (CSP) is very successful in constructing spatial filters for detecting event-related synchronization and event-related desynchronization. In statistics, a CSP filter can optimally separate the motor-imagery-related components. However, for a single trail, the EEG features extracted after a CSP filter still include features not related to motor imagery. In this study, we introduce a linear dynamical system (LDS) approach to motor-imagery-based brain-computer interface (MI-BCI) to reduce the influence of these unrelated EEG features. This study is conducted on a BCI competition data set, which comprises EEG signals from several subjects performing various movements. Experimental results show that our proposed algorithm with LDS performs better than a traditional algorithm on average. The results reveal a promising direction in the application of LDS-based approach to MI-BCI.

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Vigilance, alertness, or sustained attention: physiological basis and measurement

Cited by 635 publications

References 237 publications

Assessing Drivers’ Fatigue State Under Real Traffic Conditions Using EEG Alpha Spindles

Assessing Drivers’ Fatigue State Under Real Traffic Conditions Using EEG Alpha Spindles

Tracking brain arousal fluctuations with fMRI

Removing Unrelated Features Based on Linear Dynamical System for Motor-Imagery-Based Brain-Computer Interface

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