EEG alpha phase shifts during transition from wakefulness to drowsiness

Kalauzi, Aleksandar; Vučković, Aleksandra; Bojić, Tijana

doi:10.1016/j.ijpsycho.2012.04.012

Cited by 26 publications

(30 citation statements)

References 55 publications

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“…3 Of note, this is a typical neurophysiological feature of reduced vigilance in healthy individuals. 4 In agreement with this finding, previous data-based on less accurate EEG mapping technique-suggested an increase the central-frontal alpha. 5 Accordingly to Kullmann et al 6 the very initial stage of HE may be also characterized by an increase in frontal beta activity.…”

Section: The Eegsupporting

confidence: 76%

Clinical Neurophysiology of Hepatic Encephalopathy

Amodio

Montagnese

2015

Journal of Clinical and Experimental Hepatology

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Background/Objectives: Hepatic encephalopathy (HE) has relevant impact on the quality of life of patients and their caregivers and causes relevant costs because of hospitalizations and work days lost. Its quantification is important to perform adequate clinical trials on this relevant complication of cirrhosis and portal-systemic shunting. Clinical neurophysiology, which detects functional alterations of the nervous system, has been applied to the study of HE for over 60 years. This review aims at summarizing and clarifying the role of neurophysiologic techniques in the study of HE. Methods: A narrative review was performed aiming at interpreting the cited papers and the techniques on the basis of their physiological and pathophysiological meaning. Results: The potential role of EEG, quantified EEG, evoked potentials-both exogenous, endogenous and motor-have been clarified to the reader that may be unfamiliar with neurophysiology. Conclusions: The EEG, reflecting the oscillatory changes of neural network is the preferable tool to detect and monitor HE, with the exception of its most severe stage, when EEG flattens. SSEP and MEP have indication to detect and monitor transmission alterations that are likely related to myelin changes and microedema. ( J CLIN EXP HEPATOL 2014;5:S60-S68)

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Section: The Eegsupporting

confidence: 76%

Clinical Neurophysiology of Hepatic Encephalopathy

Amodio

Montagnese

2015

Journal of Clinical and Experimental Hepatology

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“…Several methods have been developed to characterize alpha power changes associated with various tasks. These methods include fast Fourier transforms (FFT) [7,10,11], wavelets [14], phase [15], matching pursuit [16,17], ERD/ERS [5], autoregressive modeling [6,18,19], adaptive filtering [20], neural network analysis [21], fuzzy systems [22,23], and nonlinear EEG analyses [7,24]. Other characterizations of alpha activity are the alpha band power of the signal [10] and power ratios such as the (alpha + theta)/beta ratio.…”

Section: Introductionmentioning

confidence: 99%

Detecting alpha spindle events in EEG time series using adaptive autoregressive models

2013

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BackgroundRhythmic oscillatory activity is widely observed during a variety of subject behaviors and is believed to play a central role in information processing and control. A classic example of rhythmic activity is alpha spindles, which consist of short (0.5-2 s) bursts of high frequency alpha activity. Recent research has shown that alpha spindles in the parietal/occipital area are statistically related to fatigue and drowsiness. These spindles constitute sharp changes in the underlying statistical properties of the signal. Our hypothesis is that change point detection models can be used to identify the onset and duration of spindles in EEG. In this work we develop an algorithm that accurately identifies sudden bursts of narrowband oscillatory activity in EEG using techniques derived from change point analysis. Our motivating example is detection of alpha spindles in the parietal/occipital areas of the brain. Our goal is to develop an algorithm that can be applied to any type of rhythmic oscillatory activity of interest for accurate online detection.MethodsIn this work we propose modeling the alpha band EEG time series using discounted autoregressive (DAR) modeling. The DAR model uses a discounting rate to weigh points measured further in the past less heavily than points more recently observed. This model is used together with predictive loss scoring to identify periods of EEG data that are statistically significant.ResultsOur algorithm accurately captures changes in the statistical properties of the alpha frequency band. These statistical changes are highly correlated with alpha spindle occurrences and form a reliable measure for detecting alpha spindles in EEG. We achieve approximately 95% accuracy in detecting alpha spindles, with timing precision to within approximately 150 ms, for two datasets from an experiment of prolonged simulated driving, as well as in simulated EEG. Sensitivity and specificity values are above 0.9, and in many cases are above 0.95, for our analysis.ConclusionModeling the alpha band EEG using discounted AR models provides an efficient method for detecting oscillatory alpha activity in EEG. The method is based on statistical principles and can generally be applied to detect rhythmic activity in any frequency band or brain region.

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“…All the EEG recordings were visually inspected and discriminated using EEGLAB to eliminate any ocular, muscular, and other types of artefacts before further analysis. The final EEG signals, consisting of 10 minute artefact-free periods, were analyzed in MATLAB R2011a, using software originally developed in MATLAB 6.5 (Ciric et al, 2015(Ciric et al, , 2016Kalauzi et al, 2012;Lazic et al, 2015;2017;Petrovic et al, 2013aPetrovic et al, , 2013bPetrovic et al, , 2014Saponjic et al, 2013). We particularly analyzed the EEGs derived from the four lateral frontal (F3, F7, F4, F8), and corresponding lateral posterior (P3, P4, T5, T6) electrodes (Schleiger et al, 2014).…”

Section: Eeg Data Analysismentioning

confidence: 99%

“…Manuscript to be reviewed subacute stage of the stroke vs. the control, the chronic stage of the stroke vs. the control, and the chronic stage of stroke vs. the subacute stage of stroke) for each electrode. All the calculations (the alpha carrier frequency PSs, the alpha carrier frequency PPs, the group mean alpha carrier frequency PPs and the group alpha carrier frequency PP differences) were done using angular arithmetic (Kalauzi et al, 2012, Appendix A, C, E). Since alpha carrier frequency PP is an integral phase measure of a particular EEG channel in the alpha range, incorporating all alpha Fourier components, it is possible to compare and subtract carrier frequency PP values even if the ɑAVG frequency differs between groups, as was the case in this study.…”

Section: Eeg Data Analysismentioning

confidence: 99%

Peer Review #1 of "Slower EEG alpha generation, synchronization and “flow”—possible biomarkers of cognitive impairment and neuropathology of minor stroke (v0.1)"

2017

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Background: We investigated EEG rhythms, particularly alpha activity, and their relationship to post-stroke neuropathology and cognitive functions in the subacute and chronic stages of minor strokes. Methods: We included 10 patients with right middle cerebral artery (MCA) ischemic strokes and 11 healthy controls. All the assessments of stroke patients were done both in the subacute and chronic stages. Neurological impairment was measured using the National Institute of Health Stroke Scale (NIHSS), whereas cognitive functions were assessed using the Montreal Cognitive Assessment (MoCA) and MoCA memory index (MoCA-MIS). The EEG was recorded using a 19 channel EEG system with standard EEG electrode placement. In particular, we analyzed the EEGs derived from the four lateral frontal (F3,F7,F4,F8), and corresponding lateral posterior (P3,P4,T5,T6) electrodes. Quantitative EEG analysis included: the group FFT spectra, the weighted average of alpha frequency (ɑAVG), the group probability density distributions of all conventional EEG frequency band relative amplitudes (EEG microstructure), the interand intra-hemispheric coherences, and the topographic distribution of alpha carrier frequency phase potentials (PPs). Statistical analysis was done using a Kruskal-Wallis ANOVA with a post-hoc Mann-Whitney U two-tailed test, and Spearman's correlation. Results: We demonstrated transient cognitive impairment alongside a slower alpha frequency (αAVG) in the subacute right MCA stroke patients vs. the controls. This slower alpha frequency showed no amplitude change, but was highly synchronized intrahemispherically, overlying the ipsi-lesional hemisphere, and inter-hemispherically, overlying the frontal cortex. In addition, the disturbances in EEG alpha activity in subacute stroke patients were expressed as a decrease in alpha PPs over the frontal cortex and an

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EEG alpha phase shifts during transition from wakefulness to drowsiness

Cited by 26 publications

References 55 publications

Clinical Neurophysiology of Hepatic Encephalopathy

Clinical Neurophysiology of Hepatic Encephalopathy

Detecting alpha spindle events in EEG time series using adaptive autoregressive models

Peer Review #1 of "Slower EEG alpha generation, synchronization and “flow”—possible biomarkers of cognitive impairment and neuropathology of minor stroke (v0.1)"

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