Measurement of phase gradients in the EEG

Alexander, David; Trengove, Chris; Wright, J. J.; Boord, Peter; Gordon, Evian

doi:10.1016/j.jneumeth.2006.02.016

Cited by 30 publications

(51 citation statements)

References 32 publications

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“…This would be the case if there were waves that travel across the surface of the scalp (e.g., (Alexander et al, 2006)) or perhaps when there is a relationship between activities in two distant sites (Varela et al, 2001). Neither of these conditions appears to be present in the present study, which focused entirely on data from sensorimotor cortex.…”

Section: Discussionmentioning

confidence: 99%

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

Krusienski

McFarland

Wolpaw

2012

Brain Research Bulletin

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Measures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined Phase-Locking Value (PLV) as a possible feature for use in brain-computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of interchannel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source.

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Section: Discussionmentioning

confidence: 99%

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

Krusienski

McFarland

Wolpaw

2012

Brain Research Bulletin

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“…Having a small number of wavelet cycles permits estimating EEG power and phase in short time windows, albeit at the expense of frequency resolution (Alexander, Trengove, Wright, Boord, & Gordon, 2006). The wavelet duration was 127 ms at 5 Hz and 14 ms at 45 Hz.…”

Section: Eeg Analysismentioning

confidence: 99%

Combining EEG and eye movement recording in free viewing: Pitfalls and possibilities

Nikolaev

Meghanathan

Leeuwen

2016

Brain and Cognition

104

151

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Co-registration of EEG and eye movement has promise for investigating perceptual processes in free viewing conditions, provided certain methodological challenges can be addressed. Most of these arise from the self-paced character of eye movements in free viewing conditions. Successive eye movements occur within short time intervals. Their evoked activity is likely to distort the EEG signal during fixation. Due to the non-uniform distribution of fixation durations, these distortions are systematic, survive across-trials averaging, and can become a source of confounding. We illustrate this problem with effects of sequential eye movements on the evoked potentials and time-frequency components of EEG and propose a solution based on matching of eye movement characteristics between experimental conditions. The proposal leads to a discussion of which eye movement characteristics are to be matched, depending on the EEG activity of interest. We also compare segmentation of EEG into saccade-related epochs relative to saccade and fixation onsets and discuss the problem of baseline selection and its solution. Further recommendations are given for implementing EEG-eye movement co-registration in free viewing conditions. By resolving some of the methodological problems involved, we aim to facilitate the transition from the traditional stimulus-response paradigm to the study of visual perception in more naturalistic conditions.

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“…One of the few studies was performed by Lilly and colleagues, who found that auditory evoked potentials in cats spread in different directions from the focus of activity initiated by acoustical stimulation (Lilly and Cherry 1954). In a recent study, Alexander et al (2006) documented a close correspondence between traveling waves and ERP latencies.…”

Section: Introductionmentioning

confidence: 99%

P1 and Traveling Alpha Waves: Evidence for Evoked Oscillations

Klimesch¹,

Hanslmayr²,

Sauseng³

et al. 2007

Journal of Neurophysiology

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Klimesch W, Hanslmayr S, Sauseng P, Gruber WR, Doppelmayr M. P1 and traveling alpha waves: evidence for evoked oscillations. J Neurophysiol 97: [1311][1312][1313][1314][1315][1316][1317][1318] 2007. First published December 13, 2006; doi:10.1152/jn.00876.2006. The hypothesis is tested whether the P1 of the event-related potential (ERP) component behaves like an evoked, traveling alpha wave. This hypothesis is based on different kinds of evidence showing, e.g., that-after undergoing phase reorganization-frequencies in the broad alpha range become synchronized (aligned) in absolute phase and contribute significantly to the generation of the P1. We investigated data from a Stroop task in which subjects had to respond only to the color and ignore the meaning of the presented words. Analyzing topographical phase relationships expressed in terms of traveling speed (with respect to Pz as trailing site) revealed that a systematic posterior to anterior traveling pattern appeared only in the broad time window of the P1-N1 complex and in the extended alpha frequency range. The obtained findings are consistent with the oscillatory ERP model and suggest that the P1 component may be considered a manifestation of an evoked, traveling alpha wave. We assume that the P1 reflects a top-down process in a sense that traveling alpha waves control or "gate" the direction of information processing in the brain. In this paper, we focus on P1 latency and the hypothesis that the P1 component behaves like an evoked, traveling alpha wave. This hypothesis is based on the following evidence, linking alpha and early evoked activity. First, we have observed that P1 latency is correlated with individual alpha frequency (IAF) in a sense that short latencies are associated with high IAF and long latencies with low IAF (Klimesch et al. 2004). Second, it was found that evoked oscillations-particularly in the alpha and theta frequency range-become synchronized (aligned) in absolute phase during small time windows that coincide with the latencies of the P1-N1 complex (Gruber et al. 2005;Mormann et al. 2005). We were able to demonstrate that the latencies of the P1 and N1 can be predicted at least in part by a phase alignment between different frequencies particularly in the alpha but also theta frequency range. Third, analyzing m:n phase coupling revealed that coupling between alpha and theta was largest during the time window of the P1-N1 complex (Schack et al. 2005). Fourth, growing evidence supports the oscillatory EEG model, suggesting that early evoked potentials (and the P1 in particular) are generated (at least in part) by a reorganization of phase and not by a superposition of an evoked component on ongoing oscillations with random phase (Barry et al. 2000;Basar 1999;Hanslmayr et al. 2007).These findings suggest that the P1 may be described in terms of an evoked oscillation and raise an interesting question with respect to the topography of P1 latency differences. If the P1 is generated (at least in part) by oscillations in the alpha frequenc...

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Measurement of phase gradients in the EEG

Cited by 30 publications

References 32 publications

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

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

Combining EEG and eye movement recording in free viewing: Pitfalls and possibilities

P1 and Traveling Alpha Waves: Evidence for Evoked Oscillations

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