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Open AccessJournal ArticleDOI

Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude.

Walter J. Freeman
- 01 Sep 2004 - 
- Vol. 115, Iss: 9, pp 2077-2088
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TLDR
Derivation and interpretation of unit data in studies of perception might benefit from using multichannel EEG recordings to define distinctive epochs that are demarcated by state transitions of neocortical dynamics in the CS-CR intervals, particularly in consideration of the possibility that EEG may reveal recurring episodes of exchange and sharing of perceptual information among multiple sensory cortices.
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This article is published in Clinical Neurophysiology.The article was published on 2004-09-01 and is currently open access. It has received 253 citations till now. The article focuses on the topics: Primary sensory areas & Electroencephalography.

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Coherent states, fractals and brain waves

TL;DR: It is shown that a functional representation of self-similarity (as the one occurring in fractals) is provided by squeezed coherent states and the dissipative model of brain is shown to account for the self-Similarity in brain background activity suggested by power-law distributions of power spectral densities of electrocorticograms.
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Markers of criticality in phase synchronization.

TL;DR: Proof of principle is demonstrated by analysing pairs of human simultaneous EEG and EMG time series, suggesting that LRTCs of corticomuscular phase synchronization can be detected in the resting state and experimentally manipulated.
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Dynamics of spontaneous transitions between global brain states

TL;DR: Greater predictability (deterministicity) and heterogeneity in the dynamics than what was expected from corresponding surrogate series in which linear correlations are retained are found.
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The human subthalamic nucleus encodes the subjective value of reward and the cost of effort during decision-making.

TL;DR: Low-frequency neuronal activity in the subthalamic nucleus may encode the information required to make cost-benefit comparisons, rather than signal conflict, which is consistent with the view that Parkinson's disease symptoms may be caused by a disruption of the processes involved in balancing the value of actions with their associated effort cost.
Journal ArticleDOI

Asynchrony from synchrony: long-range gamma-band neural synchrony accompanies perception of audiovisual speech asynchrony

TL;DR: Electroencephalographic evidence is provided of a phase-synchronous gamma-oscillatory network that is transiently activated by the perception of audiovisual speech asynchrony, showing both topological and time–course correspondence to networks reported in previous neuroimaging research.
References
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Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Book

Synchronization: A Universal Concept in Nonlinear Sciences

TL;DR: This work discusseschronization of complex dynamics by external forces, which involves synchronization of self-sustained oscillators and their phase, and its applications in oscillatory media and complex systems.
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Measuring phase synchrony in brain signals

TL;DR: It is argued that whereas long‐scale effects do reflect cognitive processing, short‐scale synchronies are likely to be due to volume conduction, and ways to separate such conduction effects from true signal synchrony are discussed.
Book

Synergetics: An Introduction

Hermann Haken
TL;DR: What do you do to start reading synergetics an introduction?
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Q1. What contributions have the authors mentioned in the paper "Origin, structure, and role of background eeg activity. part 1. analytic amplitude" ?

In this paper, the authors measured the phase of EEG signals with respect to the phase at a shared frequency and calculated the standard deviation ( SDX ) of the spatial distribution of the phase. 

In dynamic models of neuron populations expressed in differential equations, the order parameter is represented by a feedback gain coefficient, k (Freeman, 1975). 

The index based on Shannon entropy defined phase locking as a peak in the distribution of the phase differences between pairs of traces within a sliding window:e(t) = ! pj j=1N" ln pj (4)where pj was the relative frequency of finding the phase mod 2π within the j-th bin, and e varied between zero and ê = ln N, the number of bins (e.g. 100 bins of 0.06 radians between -π and +π radians). 

The analysis was done with MATLAB software, which has excellent graphics capabilities but is slow in computation; hence analysis was restricted to an adequate subset of the available data. 

The index was generalized to multiple channels by calculating the distribution of phase differences over all pairs of channels, after subtracting the means for each pair within the sliding window. 

The phase slip that was revealed by upward or downward deviations from the mean differences, Δpj(t), tended to occur synchronously across the entire 8x8 array, here plotted in a compressed display of Δpj(t) in the order of channel number. 

This synchronization index as a function of time was normalized, q(t) = ( ê-e(t))/ê, (5) so that q(t) was zero for a uniform distribution, and one for a delta distribution of phase values. 

the level of covariance among the EEG signals from arrays up to 1 cm in width was high; the fraction of the variance in the first component of principal components analysis (PCA) usually exceeded 95%. 

The combined mean lag of 28 ms lay within the range of three estimates previously derived from the Fourier method for the delay between formation of a spatial pattern of phase modulation and establishment of the spatial pattern of amplitude modulation: 24-34 ms (Freeman, 2003b). 

The analytic amplitude for each channel, Aj(t), was the length of the vector, which was given by the square root of the sums of squares of the real and the imaginary parts for each channel. 

When they form lines that deviate from the direction of the right abscissa (as most clearly at about -250 ms) there is a phase gradient across the array. 

The synchrony among multiple EEG records can be estimated by measuring the phase of each signal with respect to the phase of the spatial ensemble average at a shared frequency and calculating the standard deviation (SDX) of the spatial distribution of the phase (Freeman, Burke and Holmes, 2003; Part 2). 

Yet the amplitude of that component, however chaotic the wave form might be, varied with electrode location in the array so as to constitute a spatial pattern of amplitude modulation (AM) of the shared wave form.