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.

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 Tracking Economic Value of Products in Natural Settings: A Wireless EEG Study

2018 ◽  
Vol 12 ◽  
Author(s):  
Hannah Roberts ◽  
Vicente Soto ◽  
John Tyson-Carr ◽  
Katerina Kokmotou ◽  
Stephanie Cook ◽  
...  
Keyword(s):  
Economic Value ◽  
Natural Settings ◽  
Wireless Eeg

scholarly journals Time Series of Daily Averaged Cloud Fractions over Landfast First-Year Sea Ice from Multiple Data Sources

2007 ◽  
Vol 46 (11) ◽  
pp. 1818-1827 ◽  
Author(s):  
Xin Jin ◽  
John M. Hanesiak ◽  
David G. Barber
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Correlation Coefficients ◽  
Power Spectral Analysis ◽  
Early Spring ◽  
Late Winter ◽  
Phase Lag ◽  
Terra Modis ◽  
Environmental Prediction ◽  
High Clouds

Abstract The time series of daily averaged cloud fractions (CFs) collected from different platforms—two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on Terra and Aqua satellites, the National Centers for Environmental Prediction (NCEP) model, a Vaisala 25K laser ceilometer, and ground-based manual observations (manobs)—above the winter camp of the Canadian Arctic Shelf Exchange Study (CASES) field experiment are analyzed in this study. Taking the manobs as standard, the authors conclude that 1) the NCEP products considerably underestimated CFs in spring (e.g., from April to May) and 2) the performance of two MODIS products depends on the variation of solar zenith angle (SZA). Aqua MODIS misrepresents the snow-covered surface as clouds with almost randomly distributed CFs during the dark winter [cos(SZA) < 0], leading to the overestimation of CFs in winter while Terra MODIS has good agreement with manobs. When 0.1 < cos(SZA) < 0.4, both MODIS products regularly misrepresent the snow-covered background as clouds, leading to the significant overestimation of CFs in late winter (February) and early spring (March). When cos(SZA) > 0.4, both MODIS products have good performance in detecting cloud masks over snow backgrounds. If the sky is slightly cloudy, surface-based meteorological observers tend to underestimate cloud amounts when there is a lack of light. Comparing the CFs from Terra and manobs, the authors conclude that this bias can be over 10%. Power spectral analysis and wavelet analysis show three results: 1) High clouds more frequently appear in winter than in spring with periods between 8 and 16 days, indicating their close connection with synoptic events. Current NCEP products can predict this periodicity but have a phase lag. 2) Middle and low clouds are more local and are common in mid- and late spring (April and May) with periods between 2 and 4 days. At the CASES winter and spring field site, the periodicity of high clouds is dominant. 3) The time-scale-dependent correlation coefficients (CCs) between both MODIS products, NCEP and manobs, show that with high frequent CF sampling per day, the CCs are stable when the time scale varies between 1 and 4 days: with Terra MODIS and NCEP, the value is about 0.6; with Aqua MODIS, between 0.4 and 0.5. All CCs get smaller when the time scale increases beyond 8 days: with respect to both MODIS products, the CCs get closer with values between 0.3 and 0.4; with respect to NCEP, the CC dramatically decreases from positive values to negative values, indicating the lack of accuracy in current NCEP cloud schemes.

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.

scholarly journals Новый подход к экспериментальному исследованию больших ансамблей радиотехнических генераторов со сложными связями

2020 ◽  
Vol 46 (4) ◽  
pp. 26 ◽  
Author(s):  
Д.Д. Кульминский ◽  
В.И. Пономаренко ◽  
И.В. Сысоев ◽  
М.Д. Прохоров
Keyword(s):  
Time Series ◽  
Complex Dynamics ◽  
Experimental Setup ◽  
New Approach ◽  
Analog To Digital ◽  
Complex Topology ◽  
Different Types ◽  
Large Ensembles ◽  
Experimental Time Series ◽  
Time Delayed Feedback

A new approach is proposed that allows one to investigate experimentally complex dynamics in large ensembles of coupled radio engineering generators. The approach is used for the construction of an analog-to-digital experimental setup for studying ensembles of generators with time-delayed feedback. A possibility of specifying an arbitrary architecture of couplings and different types of couplings between generators is implemented in the setup. It is shown that the complex topology of couplings and parameters of all generators can be reconstructed from their experimental time series.

scholarly journals Improvement of an existing method of asynchronous sampling for determining RMS value

2018 ◽  
Vol 15 (1) ◽  
pp. 13-28
Author(s):  
Stefan Mirkovic ◽  
Dragan Pejic ◽  
Marjan Urekar ◽  
Bojan Vujicic ◽  
Djordje Novakovic
Keyword(s):  
Sampling Time ◽  
General Definition ◽  
Periodic Signal ◽  
Analog To Digital ◽  
Average Value ◽  
Asynchronous Sampling ◽  
A Value ◽  
Asynchronous Method ◽  
Initial Sampling ◽  
Definition Of

Measuring a value by definition seems to be the best solution for getting the most precise results. In this paper, it is shown that by changing the general definition of root mean square (RMS) and average value of a periodic signal, their measurement can be improved. When measuring RMS with asynchronous sampling, it was observed that results were scattered around two values, and it was found that the main cause for this was initial sampling time. Redefining the RMS value has been proposed in order to increase the efficiency of the asynchronous method. After comparing the results based on general and proposed definition for RMS value, it was observed that there was significantly less scattering of results and higher accuracy. The simulation proved that the application of the proposed definition of RMS gives more accurate and precise results than the general definition for analog to digital (AD) converters with both lower and higher resolution.

A triple-channel incremental zoom-ADC for 3-DoF MEMS digital gyroscopes

2020 ◽  
Vol 34 (13) ◽  
pp. 2050136
Author(s):  
Risheng Lv ◽  
Weiping Chen ◽  
Qiang Fu ◽  
Liang Yin ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Cmos Technology ◽  
Hybrid Architecture ◽  
Dynamic Element Matching ◽  
Loop Filter ◽  
Sigma Delta ◽  
Analog To Digital ◽  
Synchronous Sampling ◽  
On Chip

This paper presents a multiplexed analog-to-digital converter (ADC) consisting mainly of high-precision sampling holders (S/H) and an incremental zoom ADC. Flip-around design is employed in S/H modules for power economy and noise suppression. Based on efficient coordination between S/H and multiplexers, synchronous sampling is available in the whole triple-channel ADC to maintain phase accordance. The core converter employed a hybrid architecture of successive approximation register (SAR) and Sigma-Delta [Formula: see text], which constitutes an energy-efficient zoom ADC. Final conversion result is a combination of the two steps. Both the SAR and [Formula: see text] modulation share a third-order loop filter to compromise between systematic stability and input range. On-chip digital logic include capacitor array controlling and dynamic-element-matching (DEM) technique. Manufactured in a standard [Formula: see text]m CMOS technology, the whole chip occupies an area of 2.7 mm2. Experimental results show a maximum signal-to-noise ratio (SNR) of 100.2 dB, with a power consumption of 2.1 mW from a 5 V supply.

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