EEG alpha power and coherence time courses in a sustained attention task

Kelly, Simon P.; Dockree, Paul M.; Reilly, Richard B.; Robertson, Ian H.

doi:10.1109/cne.2003.1196761

Cited by 15 publications

(10 citation statements)

References 10 publications

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“…The electric mechanisms of these interactions have been assessed with various measures of brain electric (EEG) functional connectivity (e.g., David and Friston, 2003; Kelly et al, 2003; Pascual-Marqui, 2007; Srinivasan et al, 2007; Stam et al, 2007). Measures of functional connectivity for the assessment of brain states lately have received much attention.…”

Section: Introductionmentioning

confidence: 99%

sLORETA intracortical lagged coherence during breath counting in meditation-naÃ¯ve participants

Milz¹,

Faber²,

Lehmann³

et al. 2014

Front. Hum. Neurosci.

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We investigated brain functional connectivity comparing no-task resting to breath counting (a meditation exercise but given as task without referring to meditation). Functional connectivity computed as EEG coherence between head-surface data suffers from localization ambiguity, reference dependence, and overestimation due to volume conduction. Lagged coherence between intracortical model sources addresses these criticisms. With this analysis approach, experienced meditators reportedly showed reduced coherence during meditation, meditation-naïve participants have not yet been investigated. 58-channel EEG from 23 healthy, right-handed, meditation-naïve males during resting [3 runs] and breath counting [2 runs] was computed into sLORETA time series of intracortical electrical activity in 19 regions of interest (ROI) corresponding to the cortex underlying 19 scalp electrode sites, for each of the eight independent EEG frequency bands covering 1.5–44 Hz. Intracortical lagged coherences and head-surface conventional coherences were computed between the 19 regions/sites. During breath counting compared to resting, paired t-tests corrected for multiple testing revealed four significantly lower intracortical lagged coherences, but four significantly higher head-surface conventional coherences. Lowered intracortical lagged coherences involved left BA 10 and right BAs 3, 10, 17, 40. In conclusion, intracortical lagged coherence can yield results that are inverted to those of head-surface conventional coherence. The lowered functional connectivity between cognitive control areas and sensory perception areas during meditation-type breath counting compared to resting conceivably reflects the attention to a bodily percept without cognitive reasoning. The reductions in functional connectivity were similar but not as widespread as the reductions reported during meditation in experienced meditators.

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Section: Introductionmentioning

confidence: 99%

sLORETA intracortical lagged coherence during breath counting in meditation-naÃ¯ve participants

Milz¹,

Faber²,

Lehmann³

et al. 2014

Front. Hum. Neurosci.

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“…Sustained attention is an important cognitive function in human behavior, having influence on other attentional aspects (selective attention, divided attention). In the past few years, the cognitive researchers have devoted themselves to understanding the sustained attention process within the human brain so as to understand the neuronal mechanisms underlying this cognitive function and to be sensitive to discrete information processing impairments that were often associated with clinical disorders, such as Attention Deficit Hyperactivity Disorder (ADHD) and Traumatic Brain Injury (TBI) [4][5][6]. The brain imaging studies have demonstrated that the activation of frontal and parietal cortical areas, especially in the right hemisphere, is associated with the sustained attention performance [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of propagation of multi-channel EEG in the test of sustained attention

Yan

Wang

Xue

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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the psychological construct 'sustained attention' describes a basic component of attention characterized by the subject's readiness to detect rarely and unpredictably occurring signals over prolonged periods of time. In this study, six healthy volunteers underwent a sustained attention to response task (SART), while their electroencephalographic (EEG) were recorded contemporarily. Directed Transfer Function (DTF) was used as estimator for direction of propagation of EEG function coupling. The results of DTF showed that the information flux within EEG functional coupling changed when attention condition changed from inattention state to sustained attention state, principally at alpha and beta rhythms. The DTF could be used to evaluate sustained attention condition and they might be used for research on damage of attention mechanisms of ADHD and TBI diseases in future.

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“…Many other studies focused on specific EEG rhythms. In [2], alpha band activities and coherence time courses were examined in an attention study. In [3], experimental results also indicated correlates between alpha power dynamics and errors by subjects in sustained attention tasks.…”

Section: Introductionmentioning

confidence: 99%

Learning EEG-based spectral-spatial patterns for attention level measurement

Hamadicharef

Zhang

Guan

et al. 2009

2009 IEEE International Symposium on Circuits and Systems

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Abstract-In our every day life, our brain is constantly processing information and paying attention, reacting accordingly, to all sorts of sensory inputs (auditory, visual, etc.). In some cases, there is a need to accurately measure a person's level of attention to monitor a sportsman performance, to detect Attention Deficit Hyperactivity Disorder (ADHD) in children, to evaluate the effectiveness of neuro-feedback treatment, etc.In this paper we propose a novel approach to extract, select and learn spectral-spatial patterns from electroencephalogram (EEG) recordings. Our approach improves over prior-art methods that was, typically, only concerned with power of specific EEG rhythms from few individual channels. In this new approach, spectral-spatial features from multichannel EEG are extracted by a two filtering stages: a filter-bank (FB) and common spatial patterns (CSP) filters. The most important features are selected by a Mutual Information (MI) based feature selection procedure and then classified using Fisher linear discriminant (FLD). The outcome is a measure of the attention level.An experimental study was conducted with 5 healthy young male subjects with their EEG recorded in various attention and non-attention conditions (opened eyes, closed eyes, reading, counting, relaxing, etc.). EEGs were used to train and evaluate the model using 4x4fold cross-validation procedure. Results indicate that the new proposed approach outperforms the prior-art methods and can achieve up to 89.4% classification accuracy rate (with an average improvement of up to 16%). We demonstrate its application with a two-players attention-based racing car computer game.

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EEG alpha power and coherence time courses in a sustained attention task

Cited by 15 publications

References 10 publications

sLORETA intracortical lagged coherence during breath counting in meditation-naÃ¯ve participants

sLORETA intracortical lagged coherence during breath counting in meditation-naÃ¯ve participants

Analysis of propagation of multi-channel EEG in the test of sustained attention

Learning EEG-based spectral-spatial patterns for attention level measurement

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