Literature DB >> 16574240

Measurement of phase gradients in the EEG.

D M Alexander1, C Trengove, J J Wright, P R Boord, E Gordon.   

Abstract

Previous research has shown that spatio-temporal waves in the EEG are generally of long spatial wavelength and form smooth patterns of phase gradients at particular time-samples. This paper describes a method to measure smooth phase gradients of long spatial wavelength in the EEG. The method depends on the global pattern of phase at a given frequency and time and is therefore robust to variations, over time, in phase-lag between particular sites. Phases were estimated in the EEG signal using wavelet or short time-series Fourier methods. During an auditory oddball task, phases across the scalp tend to fall within a limited circular range, a range that is not indicative of phase-synchrony nor waves with multiple periods. At times the phases tended to maintain a spatially and temporally ordered relationship. The relative phases were analysed using three phase gradient basis functions, providing a measure of the amount of variance explained, across the electrodes, by smooth changes in relative phase from a single minimum or single maximum. The data from 586 adult subjects were analysed and it was found that the probability of phase gradient events varies with time and frequency in the stimulus-locked average, and with task demands. The temporal extent of spatio-temporal waves was measured by detecting smoothly changing patterns of phase latencies across the scalp. The specific spatial pattern and timing of phase gradients correspond closely to the latency distributions of certain ERPs.

Entities:  

Mesh:

Year:  2006        PMID: 16574240     DOI: 10.1016/j.jneumeth.2006.02.016

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  12 in total

1.  Generalization of learning by synchronous waves: from perceptual organization to invariant organization.

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2.  Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface.

Authors:  Dean J Krusienski; Dennis J McFarland; Jonathan R Wolpaw
Journal:  Brain Res Bull       Date:  2011-10-01       Impact factor: 4.077

3.  Human cortical traveling waves: dynamical properties and correlations with responses.

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Journal:  PLoS One       Date:  2012-06-04       Impact factor: 3.240

4.  Evoked traveling alpha waves predict visual-semantic categorization-speed.

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Journal:  Neuroimage       Date:  2011-11-10       Impact factor: 6.556

5.  Prestimulus amplitudes modulate P1 latencies and evoked traveling alpha waves.

Authors:  Nicole A Himmelstoss; Christina P Brötzner; Andrea Zauner; Hubert H Kerschbaum; Walter Gruber; Julia Lechinger; Wolfgang Klimesch
Journal:  Front Hum Neurosci       Date:  2015-05-27       Impact factor: 3.169

6.  Donders is dead: cortical traveling waves and the limits of mental chronometry in cognitive neuroscience.

Authors:  David M Alexander; Chris Trengove; Cees van Leeuwen
Journal:  Cogn Process       Date:  2015-07-03

7.  Large-scale cortical travelling waves predict localized future cortical signals.

Authors:  David M Alexander; Tonio Ball; Andreas Schulze-Bonhage; Cees van Leeuwen
Journal:  PLoS Comput Biol       Date:  2019-11-15       Impact factor: 4.475

Review 8.  Is neuroimaging measuring information in the brain?

Authors:  Lee de-Wit; David Alexander; Vebjørn Ekroll; Johan Wagemans
Journal:  Psychon Bull Rev       Date:  2016-10

9.  Global Neuromagnetic Cortical Fields Have Non-Zero Velocity.

Authors:  David M Alexander; Andrey R Nikolaev; Peter Jurica; Mikhail Zvyagintsev; Klaus Mathiak; Cees van Leeuwen
Journal:  PLoS One       Date:  2016-03-08       Impact factor: 3.240

10.  The shaky ground truth of real-time phase estimation.

Authors:  Christoph Zrenner; Dragana Galevska; Jaakko O Nieminen; David Baur; Maria-Ioanna Stefanou; Ulf Ziemann
Journal:  Neuroimage       Date:  2020-03-18       Impact factor: 6.556

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