scholarly journals Near-zero phase-lag hyperscanning in a novel wireless EEG system

2021 ◽  
Author(s):  
Chun-Hsiang Chuang ◽  
Shao-Wei Lu ◽  
Yiping Chao ◽  
Po-Hsun Peng ◽  
Hao-Che Hsu ◽  
...  
Keyword(s):  
Phase Lag ◽  
Wireless Eeg ◽  
Zero Phase

scholarly journals Near-Zero Phase-Lag Hyperscanning in a Novel Wireless EEG System

2021 ◽  
Author(s):  
Chun-Hsiang Chuang ◽  
Shao-Wei Lu ◽  
Yi-Ping Chao ◽  
Po-Hsun Peng ◽  
Hao-Che Hsu ◽  
...  
Keyword(s):  
Time Series ◽  
Video Gaming ◽  
Phase Lag ◽  
Analog To Digital ◽  
Synchronous Sampling ◽  
Asynchronous Sampling ◽  
Custom Made ◽  
Natural Settings ◽  
Wireless Eeg ◽  
Zero Phase

Hyperscanning is an emerging technology that concurrently scans the neural dynamics of multiple individuals to study interpersonal interactions. In particular, hyperscanning with wireless electroencephalography (EEG) is increasingly popular owing to its mobility and ability to decipher social interactions in natural settings at the millisecond scale. To align multiple EEG time series with sophisticated event markers in a single time domain, a precise and unified timestamp is required for stream synchronization. This study proposed a clock-synchronized method using a custom-made RJ45 cable to coordinate the sampling between wireless EEG amplifiers to prevent incorrect estimation of interbrain connectivity due to asynchronous sampling. In this method, analog-to-digital converters are driven by the same sampling clock. Additionally, two clock-synchronized amplifiers leverage additional RF channels to keep the counter of their receiving dongles updated, guaranteeing that binding event markers received by the dongle with the EEG time series have the correct timestamp. The results of two simulation experiments and one video gaming experiment revealed that the proposed method ensures synchronous sampling in a system with multiple EEG devices, achieving near-zero phase-lag and negligible amplitude difference between signals. According to all of the signal-similarity metrics, the suggested method is a promising option for wireless EEG hyperscanning and can be utilized to precisely assess the interbrain couplings underlying social-interaction behaviors.

A NEW METHODOLOGY FOR THE CONSTRUCTION OF OPTIMIZED RUNGE–KUTTA–NYSTRÖM METHODS

2011 ◽  
Vol 22 (06) ◽  
pp. 623-634 ◽  
Author(s):  
D. F. PAPADOPOULOS ◽  
T. E. SIMOS
Keyword(s):  
Schrödinger Equation ◽  
Schrodinger Equation ◽  
First Integrals ◽  
New Method ◽  
Phase Lag ◽  
Nyström Methods ◽  
Algebraic Order ◽  
Runge Kutta ◽  
First Time ◽  
Zero Phase

In this paper, a new Runge–Kutta–Nyström method of fourth algebraic order is developed. The new method has zero phase-lag, zero amplification error and zero first integrals of the previous properties. Numerical results indicate that the new method is very efficient for solving numerically the Schrödinger equation. We note that for the first time in the literature we use the requirement of vanishing the first integrals of phase-lag and amplification error in the construction of efficient methods for the numerical solution of the Schrödinger equation.

Gamma Oscillation Model Predicts Intensity Coding by Phase Rather than Frequency

1997 ◽  
Vol 9 (6) ◽  
pp. 1251-1264 ◽  
Author(s):  
Roger D. Traub ◽  
Miles A. Whittington ◽  
John G. R. Jefferys
Keyword(s):  
Pyramidal Cells ◽  
Sensory Stimulation ◽  
Gamma Oscillations ◽  
Feature Binding ◽  
Phase Lag ◽  
Cell Groups ◽  
Phase Lags ◽  
Zero Phase

Gamma-frequency electroencephalogram oscillations may be important for cognitive processes such as feature binding. Gamma oscillations occur in hippocampus in vivo during the theta state, following physiological sharp waves, and after seizures, and they can be evoked in vitro by tetanic stimulation. In neocortex, gamma oscillations occur under conditions of sensory stimulation as well as during sleep. After tetanic or sensory stimulation, oscillations in regions separated by several millimeters or more occur at the same frequency, but with phase lags ranging from less than 1 ms to 10 ms, depending on the conditions of stimulation. We have constructed a distributed network model of pyramidal cells and interneurons, based on a variety of experiments, that accounts for near-zero phase lag synchrony of oscillations over long distances (with axon conduction delays totaling 16 ms or more). Here we show that this same model can also account for fixed positive phase lags between nearby cell groups coexisting with near-zero phase lags between separated cell groups, a phenomenon known to occur in visual cortex. The model achieves this because interneurons fire spike doublets and triplets that have average zero phase difference throughout the network; this provides a temporal framework on which pyramidal cell phase lags can be superimposed, the lag depending on how strongly the pyramidal cells are excited.

Synchronization in Relaxation Oscillator Networks with Conduction Delays

2001 ◽  
Vol 13 (5) ◽  
pp. 1003-1021 ◽  
Author(s):  
Jeffrey J. Fox ◽  
Ciriyam Jayaprakash ◽  
DeLiang Wang ◽  
Shannon R. Campbell
Keyword(s):  
Numerical Simulations ◽  
Coupled Oscillators ◽  
Initial Conditions ◽  
Relaxation Oscillator ◽  
Phase Lag ◽  
Relaxation Oscillators ◽  
Oscillator Networks ◽  
Coupled Networks ◽  
Parameter Values ◽  
Zero Phase

We study locally coupled networks of relaxation oscillators with excitatory connections and conduction delays and propose a mechanism for achieving zero phase-lag synchrony. Our mechanism is based on the observation that different rates of motion along different nullclines of the system can lead to synchrony in the presence of conduction delays. We analyze the system of two coupled oscillators and derive phase compression rates. This analysis indicates how to choose nullclines for individual relaxation oscillators in order to induce rapid synchrony. The numerical simulations demonstrate that our analytical results extend to locally coupled networks with conduction delays and that these networks can attain rapid synchrony with appropriately chosen nullclines and initial conditions. The robustness of the proposed mechanism is verified with respect to different nullclines, variations in parameter values, and initial conditions.

How Precise is Neuronal Synchronization?

1995 ◽  
Vol 7 (3) ◽  
pp. 469-485 ◽  
Author(s):  
Peter König ◽  
Andreas K. Engel ◽  
Pieter R. Roelfsema ◽  
Wolf Singer
Keyword(s):  
Cortical Neurons ◽  
Visual Stimuli ◽  
Network Models ◽  
Movement Direction ◽  
Phase Lag ◽  
Graded Responses ◽  
Spatially Distributed ◽  
The Difference ◽  
Coarse Coding ◽  
Zero Phase

Recent work suggests that synchronization of neuronal activity could serve to define functionally relevant relationships between spatially distributed cortical neurons. At present, it is not known to what extent this hypothesis is compatible with the widely supported notion of coarse coding, which assumes that features of a stimulus are represented by the graded responses of a population of optimally and suboptimally activated cells. To resolve this issue we investigated the temporal relationship between responses of optimally and suboptimally stimulated neurons in area 17 of cat visual cortex. We find that optimally and suboptimally activated cells can synchronize their responses with a precision of a few milliseconds. However, there are consistent and systematic deviations of the phase relations from zero phase lag. Systematic variation of the orientation of visual stimuli shows that optimally driven neurons tend to lead over suboptimally activated cells. The observed phase lag depends linearly on the stimulus orientation and is, in addition, proportional to the difference between the preferred orientations of the recorded cells. Similar effects occur when testing the influence of the movement direction and the spatial frequency of visual stimuli. These results suggest that binding by synchrony can be used to define assemblies of neurons representing a coarse-coded stimulus. Furthermore, they allow a quantitative test of neuronal network models designed to reproduce physiological results on stimulus-specific synchronization.

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