scholarly journals Estimation of narrowband amplitude and phase from electrophysiology signals for phase-amplitude coupling studies: a comparison of methods

2018 ◽  
Author(s):  
Juan L.P. Soto ◽  
Felipe V.D. Prado ◽  
Etienne Combrisson ◽  
Karim Jerbi
Keyword(s):  
High Frequency ◽  
Brain Activity ◽  
Low Frequency ◽  
Preliminary Evidence ◽  
Visuomotor Coordination ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Electrophysiological Measurements ◽  
High Gamma ◽  
Phase Amplitude

AbstractMany functional connectivity studies based on electrophysiological measurements, such as electro- and magnetoencephalography (EEG/MEG), start their investigations by extracting a narrowband representation of brain activity time series, and then computing their envelope amplitudes and instantaneous phases, which serve as inputs to subsequent data processing. The two most popular approaches for obtaining these narrowband amplitudes and phases are: bandpass filtering followed by Hilbert transform (we call this the Hilbert approach); and convolution with wavelet kernels (the wavelet approach). In this work, we investigate how these two approaches perform in detecting the phenomenon of phase-amplitude coupling (PAC), whereby the amplitude of a high-frequency signal is driven by the phase of a low-frequency signal. The comparison of both approaches is carried out by means of simulated brain activity, from which we run receiver operating characteristic (ROC) analyses, and of experimental MEG data from a visuomotor coordination study. The ROC analyses show that both approaches have comparable accuracy, except in the presence of interfering signals with frequencies near the high-frequency band. As for the visuomotor data, the most noticeable impact of the choice of approach was observed when evaluating task-based changes in PAC between the delta (2-5 Hz) and the high-gamma (60-90 Hz) frequency bands, as we were able to identify widespread brain areas with statistically significant effects only with the Hilbert approach. These results provide preliminary evidence of the advantages of the Hilbert approach over the wavelet approach, at least in the context of PAC estimates.

scholarly journals An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Dongju Chen ◽  
Shuai Zhou ◽  
Lihua Dong ◽  
Jinwei Fan
Keyword(s):  
Machine Tool ◽  
Frequency Domain ◽  
Surface Topography ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Time Frequency ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method

This paper presents a new identification method to identify the main errors of the machine tool in time-frequency domain. The low- and high-frequency signals of the workpiece surface are decomposed based on the Daubechies wavelet transform. With power spectral density analysis, the main features of the high-frequency signal corresponding to the imbalance of the spindle system are extracted from the surface topography of the workpiece in the frequency domain. With the cross-correlation analysis method, the relationship between the guideway error of the machine tool and the low-frequency signal of the surface topography is calculated in the time domain.

Wavelet De-Noising Algorithm Based on OpenCV for Images Edge Detection

2014 ◽  
Vol 989-994 ◽  
pp. 3973-3976
Author(s):  
Yi Fan Ma ◽  
Shu Gui Liu
Keyword(s):  
Edge Detection ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Canny Edge Detection ◽  
Image Edge Detection ◽  
Canny Edge ◽  
Image Edge ◽  
Wavelet Algorithm

Image edge detection is easily affected by noise. Wavelet algorithm can divide the image into low frequency and high frequency. By the processing of high frequency signal and the reconstruction of wavelet coefficients, the purpose of removing noise can be achieved. In the environment of VC++6.0, an image de-noising algorithm based on the wavelet combined with the Canny edge detection is proposed, which obtains a good result. The above algorithms are implemented based on OpenCV, which is more efficient, providing the conditions for subsequent image analysis and recognition. Experiments are carried out and the results show that the proposed algorithm is available and has a good performance.

Vibrational resonance in a discrete neuronal model with time delay

2014 ◽  
Vol 28 (16) ◽  
pp. 1450103 ◽  
Author(s):  
Canjun Wang ◽  
Keli Yang ◽  
Shixian Qu
Keyword(s):  
Time Delay ◽  
Input Signal ◽  
High Frequency ◽  
Phase Synchronization ◽  
Low Frequency ◽  
Neuronal Model ◽  
Vibrational Resonance ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Neuron System

The effects of time delay on the vibrational resonance (VR) in a discrete neuron system with a low-frequency signal and a high-frequency signal are investigated by numerical simulations. The results show that there exists a delay time that optimizes the phase synchronization between the low-frequency input signal and the output signal. VR is induced by the time delay. Furthermore, the time delay can improve the response to a low-frequency input signal. Therefore, the time delay plays a constructive role in the transmission of a low-frequency signal by inducing and enhancing VR.

Simulation of Secondary Equipment Lightning Protection in EHV Substation Based on ATP

2014 ◽  
Vol 543-547 ◽  
pp. 514-517
Author(s):  
Hong Bo Li ◽  
Lin Niu ◽  
Nan Nan Gao ◽  
Jie Zhan ◽  
Pei Li ◽  
...  
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Lightning Protection ◽  
Earth System ◽  
Secondary System ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Lightning Current ◽  
Grounding Grids ◽  
The One

By analyzing the two lightning accidents happening to a 500kV substation situated in the lightning-prone northeastern Guangdong Province, I conclude as follows, there should be some problem in the equipotential earthing of the secondary system of this substation. Specifically, the existing potential difference in the secondary equipment gave rise to the meltdown of it which finally caused the accident. In the context,I build the one-point earthing parallel system simulation model according to ATP graphic pretreatment program, then by using slope-ramp simulation I study the lightning current which leaked into the grounding grids of the substation.In conclusion, the application of high frequency signal mixed low frequency signal earth system in the secondary system and the equipotential bonding is key to preventing accidents.

scholarly journals A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Jessica K Nadalin ◽  
Louis-Emmanuel Martinet ◽  
Ethan B Blackwood ◽  
Meng-Chen Lo ◽  
Alik S Widge ◽  
...  
Keyword(s):  
High Frequency ◽  
Statistical Power ◽  
Brain Activity ◽  
Low Frequency ◽  
Modeling Framework ◽  
Frequency Coupling ◽  
Phase Amplitude ◽  
Cross Frequency Coupling ◽  
Frequency Phase

Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate examples of CFC during a seizure and in response to electrical stimuli.

scholarly journals A Facile Strategy for Visualizing and Modulating Droplet-Based Microfluidics

Micromachines ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 291 ◽  
Author(s):  
Zehang Gao ◽  
Huo Peng ◽  
Minjie Zhu ◽  
Lei Wu ◽  
Chunping Jia ◽  
...  
Keyword(s):  
High Throughput ◽  
High Frequency ◽  
Imaging System ◽  
Low Cost ◽  
Low Frequency ◽  
Microfluidic Chips ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Additional Signal ◽  
And Control

In droplet-based microfluidics, visualizing and modulating of droplets is often prerequisite. In this paper, we report a facile strategy for visualizing and modulating high-throughput droplets in microfluidics. In the strategy, by modulating the sampling frequency of a flash light with the droplet frequency, we are able to map a real high frequency signal to a low frequency signal, which facilitates visualizing and feedback controlling. Meanwhile, because of not needing synchronization signals, the strategy can be directly implemented on any droplet-based microfluidic chips. The only cost of the strategy is an additional signal generator. Moreover, the strategy can catch droplets with frequency up to several kilohertz, which covers the range of most high-throughput droplet-based microfluidics. In this paper, the principle, setup and procedure were introduced. Finally, as a demonstration, the strategy was also implemented in a miniaturized picoinjector in order to monitor and control the injection dosage to droplets. We expect that this facile strategy supplies a low-cost yet effective imaging system that can be easily implemented in miniaturized microfluidic systems or general laboratories.

scholarly journals Shallow crustal imaging using distant, high-magnitude earthquakes

2019 ◽  
Vol 219 (2) ◽  
pp. 1082-1091 ◽  
Author(s):  
Johno van IJsseldijk ◽  
Elmer Ruigrok ◽  
Arie Verdel ◽  
Cornelis Weemstra
Keyword(s):  
High Frequency ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Seismic Interferometry ◽  
Sedimentary Structure ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Midcontinent Rift ◽  
High Magnitude

SUMMARY Global phases, viz. seismic phases that travel through the Earth’s core, can be used to locally image the crust by means of seismic interferometry. This method is known as Global Phase Seismic Interferometry (GloPSI). Traditionally, GloPSI retrieves low-frequency information (up to 1 Hz). Recent studies, however, suggest that there is high-frequency signal present in the coda of strong, distant earthquakes. This research quantifies the potential of these high-frequency signals, by analysing recordings of a multitude of high-magnitude earthquakes (≥6.4 Mw) and their coda on a selection of permanent USArray stations. Nearly half of the P, PKP and PKIKP phases are recorded with a signal-to-noise ratio of at least 5 dB at 3 Hz. To assess the viability of using the high-frequency signal, the second half of the paper highlights two case studies. First, a known sedimentary structure is imaged in Malargüe, Argentina. Secondly, the method is used to reveal the structure of the Midcontinent Rift below the SPREE array in Minnesota, USA. Both studies demonstrate that structural information of the shallow crust (≤5 km) below the arrays can be retrieved. In particular, the interpreted thickness of the sedimentary layer below the Malargüe array is in agreement with earlier studies in the same area. Being able to use global phases and direct P-phases with large epicentral distances (>80°) to recover the Earth’s sedimentary structure suggests that GloPSI can be applied in an industrial context.

Based on Bayesian Network of Artificial Fish of Auditory System Research

2012 ◽  
Vol 505 ◽  
pp. 305-310
Author(s):  
Xiao Yang He ◽  
Xin Li ◽  
Han Li
Keyword(s):  
Bayesian Network ◽  
Virtual Environment ◽  
Auditory System ◽  
High Frequency ◽  
Auditory Perception ◽  
Low Frequency ◽  
Acoustic Signals ◽  
System Research ◽  
Frequency Signal ◽  
High Frequency Signal

The intelligent virtual environment in an artificial fish of virtual auditory system is designed in this paper. Firstly, the model of artificial fish of auditory system in intelligent virtual environment (IVE) is built. Secondly, two-layer of artificial fish of auditory perception is designed according to the biological mechanism of auditory. The first layer is based on the design of the low frequency signal. The second layer is based on the design of the high frequency signal. And using the bayesian network(BN) to realize the acoustic signals of artificial fish to learn and memory algorithm. Get a satisfactory result through animation simulation.

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