EEG synchrony during a perceptual-cognitive task: Widespread phase synchrony at all frequencies

Pockett, Susan; Bold, Gary E. J.; Freeman, Walter J.

doi:10.1016/j.clinph.2008.12.044

Cited by 72 publications

(59 citation statements)

References 49 publications

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“…The original experiment for which the data studied here were collected (Pockett et al, 2009) was designed to investigate trial and error learning-in particular, to look for any perceptual or cognitive differences that might emerge when subjects experienced the 'aha' realization of which key should be pressed in response to each bimodal stimulus. In the present study we limited our search to the EEG patterns directly following two of the stimulus Fig.…”

Section: Discussionmentioning

confidence: 99%

“…The experimental design and recording procedure have been documented elsewhere (Pockett et al, 2009). The visual stimuli consisted of a 125 ms flash during which a computer screen turned either entirely red or entirely blue.…”

Section: Experimental Design and Signal Pre-processingmentioning

confidence: 99%

“…The results from applying the techniques developed for extraction of AM patterns from ECoG were positive (Ruiz et al, 2009a). The present report is based on data from experiments designed to facilitate the search for scalp correlates of sensory perception (Pockett et al, 2009) by having subjects engage in multisensory discrimination, in order to optimize the likelihood of multicortical synchronization (Dumenko, 2000).…”

Section: Introductionmentioning

confidence: 96%

“…(a) Suggestions that multiple modules perform cognitive functions by coordinating their dynamics (Pribram, 1971;Baars, 1998;Bressler and Kelso, 2001;Fingelkurts and Fingelkurts, 2001;Breakspear et al, 2004;Kelso and Tognoli, 2007). (b) Multichannel recordings showing widespread intermittent synchrony of oscillations in the beta and gamma range of the EEG (Freeman et al, 2003a;Pockett et al, 2009) and MEG (Stam et al, 2003;Bassett et al, 2006). (c) Numerous reports on the correlation of EEG oscillations, particularly in the gamma range, with a variety of cognitive functions (Tallon-Baudry et al, 1998;Miltner et al, 1999;Rodriguez et al, 1999;Bertrand and Tallon-Baudry, 2000;Haig et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

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A method to study global spatial patterns related to sensory perception in scalp EEG

Ruiz

Pockett

Freeman

et al. 2010

Journal of Neuroscience Methods

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a b s t r a c tPrior studies of multichannel ECoG from animals showed that beta and gamma oscillations carried perceptual information in both local and global spatial patterns of amplitude modulation, when the subjects were trained to discriminate conditioned stimuli (CS). Here the hypothesis was tested that similar patterns could be found in the scalp EEG human subjects trained to discriminate simultaneous visual-auditory CS. Signals were continuously recorded from 64 equispaced scalp electrodes and band-pass filtered. The Hilbert transform gave the analytic phase, which segmented the EEG into temporal frames, and the analytic amplitude, which expressed the pattern in each frame as a feature vector. Methods applied to the ECoG were adapted to the EEG for systematic search of the beta-gamma spectrum, the time period after CS onset, and the scalp surface to locate patterns that could be classified with respect to type of CS. Spatial patterns of EEG amplitude modulation were found from all subjects that could be classified with respect to stimulus combination type significantly above chance levels. The patterns were found in the beta range (15-22 Hz) but not in the gamma range. They occurred in three short bursts following CS onset. They were non-local, occupying the entire array. Our results suggest that the scalp EEG can yield information about the timing of episodically synchronized brain activity in higher cognitive function, so that future studies in brain-computer interfacing can be better focused. Our methods may be most valuable for analyzing data from dense arrays with very high spatial and temporal sampling rates.

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Section: Discussionmentioning

confidence: 99%

Section: Experimental Design and Signal Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A method to study global spatial patterns related to sensory perception in scalp EEG

Ruiz

Pockett

Freeman

et al. 2010

Journal of Neuroscience Methods

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“…We evaluate our method on a 64-channel EEG dataset with 6 subjects [5]. For 5 out of 6 subjects the approximation shows statistically significant classification at rates comparable to the previous algorithm [2].…”

Section: Introductionmentioning

confidence: 91%

Locating spatial patterns of waveforms during sensory perception in scalp EEG

Brockmeier

Hazrati

Freeman

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The spatio-temporal oscillations in EEG waves are indicative of sensory and cognitive processing. We propose a method to find the spatial amplitude patterns of a timelimited waveform across multiple EEG channels. It consists of a single iteration of multichannel matching pursuit where the base waveform is obtained via the Hilbert transform of a time-limited tone. The vector of extracted amplitudes across channels is used for classification, and we analyze the effect of deviation in temporal alignment of the waveform on classification performance. Results for a previously published dataset of 6 subjects show comparable results versus a more complicated criteria-based method.

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Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools

Siettos

Starke

2016

WIREs Mechanisms of Disease

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The extreme complexity of the brain naturally requires mathematical modeling approaches on a large variety of scales; the spectrum ranges from single neuron dynamics over the behavior of groups of neurons to neuronal network activity. Thus, the connection between the microscopic scale (single neuron activity) to macroscopic behavior (emergent behavior of the collective dynamics) and vice versa is a key to understand the brain in its complexity. In this work, we attempt a review of a wide range of approaches, ranging from the modeling of single neuron dynamics to machine learning. The models include biophysical as well as data-driven phenomenological models. The discussed models include Hodgkin-Huxley, FitzHugh-Nagumo, coupled oscillators (Kuramoto oscillators, Rössler oscillators, and the Hindmarsh-Rose neuron), Integrate and Fire, networks of neurons, and neural field equations. In addition to the mathematical models, important mathematical methods in multiscale modeling and reconstruction of the causal connectivity are sketched. The methods include linear and nonlinear tools from statistics, data analysis, and time series analysis up to differential equations, dynamical systems, and bifurcation theory, including Granger causal connectivity analysis, phase synchronization connectivity analysis, principal component analysis (PCA), independent component analysis (ICA), and manifold learning algorithms such as ISOMAP, and diffusion maps and equation-free techniques. WIREs Syst Biol Med 2016, 8:438-458. doi: 10.1002/wsbm.1348 For further resources related to this article, please visit the WIREs website.

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EEG synchrony during a perceptual-cognitive task: Widespread phase synchrony at all frequencies

Cited by 72 publications

References 49 publications

A method to study global spatial patterns related to sensory perception in scalp EEG

A method to study global spatial patterns related to sensory perception in scalp EEG

Locating spatial patterns of waveforms during sensory perception in scalp EEG

Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools

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