scholarly journals Analysis of EEG-EMG Coherence in Low Frequency Bands

2021 ◽  
Author(s):  
Krishnamoorthy Arunganesh ◽  
Natarajan Sivakumaran ◽  
Shanmugasundaram Kumaravel ◽  
Pa Karthick
Keyword(s):  
Lower Limb ◽  
Tibialis Anterior Muscle ◽  
Low Frequency ◽  
Body Motion ◽  
Frequency Bands ◽  
Stair Descent ◽  
Delta Band ◽  
Frequency Components ◽  
The Brain ◽  
Magnitude Squared Coherence

In this, study, an attempt is made to analyze the corticomuscular coupling of the brain and muscular system in the low-frequency components during ramp descent (RD) and stair descent (SD) locomotion. For this purpose, magnitude squared coherence (MSC) is computed from the simultaneous EEG and EMG signals recorded during the ramp and stair descent tasks. The MSC is extracted from the low- frequency bands such as delta (0.1–3 Hz) and theta bands (4–7 Hz). The study utilizes a publicly available database consisting of simultaneous recorded EEG, lower limb EMG and full body motion information from ten healthy subjects. The results show that there exists corticomuscular coupling between motor cortex (C1, C2 and Cz contacts) and tibialis anterior muscle activities during RD and SD. In addition, the MSC differs for both the tasks and frequency bands. In delta band frequencies, the MSC is found to be higher in C2 regions. In the case of theta, the MSC is higher in C1 during RD and in Cz during SD. Therefore, the MSC associated with the low frequency components could be used to detect walking intentions.

scholarly journals Coherence between brain activation and speech envelope at word and sentence levels showed age-related differences in low frequency bands

2021 ◽  
pp. 1-62
Author(s):  
Orsolya B Kolozsvári ◽  
Weiyong Xu ◽  
Georgia Gerike ◽  
Tiina Parviainen ◽  
Lea Nieminen ◽  
...  
Keyword(s):  
Speech Perception ◽  
Language Processing ◽  
Sentence Processing ◽  
Low Frequency ◽  
Frequency Bands ◽  
Age Related ◽  
Speech Envelope ◽  
Stimulus Length ◽  
The Brain ◽  
Speech Tracking

Speech perception is dynamic and shows changes across development. In parallel, functional differences in brain development over time have been well documented and these differences may interact with changes in speech perception during infancy and childhood. Further, there is evidence that the two hemispheres contribute unequally to speech segmentation at the sentence and phonemic levels. To disentangle those contributions, we studied the cortical tracking of various sized units of speech that are crucial for spoken language processing in children (4.7-9.3 year-olds, N=34) and adults (N=19). We measured participants’ magnetoencephalogram (MEG) responses to syllables, words and sentences, calculated the coherence between the speech signal and MEG responses at the level of words and sentences, and further examined auditory evoked responses to syllables. Age-related differences were found for coherence values at the delta and theta frequency bands. Both frequency bands showed an effect of stimulus type, although this was attributed to the length of the stimulus and not linguistic unit size. There was no difference between hemispheres at the source level either in coherence values for word or sentence processing or in evoked response to syllables. Results highlight the importance of the lower frequencies for speech tracking in the brain across different lexical units. Further, stimulus length affects the speech-brain associations suggesting methodological approaches should be selected carefully when studying speech envelope processing at the neural level. Speech tracking in the brain seems decoupled from more general maturation of the auditory cortex.

scholarly journals Dynamics of coherent activity between cortical areas defines a two-stage process of selective attention

2020 ◽  
Author(s):  
E. Levichkina ◽  
M. Kermani ◽  
Y.B. Saalmann ◽  
T.R. Vidyasagar
Keyword(s):  
Low Frequency ◽  
Brain Regions ◽  
Saliency Map ◽  
Top Down ◽  
Frequency Bands ◽  
Gamma Coherence ◽  
Stage Process ◽  
Frequency Coherence ◽  
Object Features ◽  
The Brain

ABSTRACTAnalysing a visual scene requires the brain to briefly keep in memory potentially relevant parts and then direct attention to their locations for detailed processing. To reveal the neuronal basis of the underlying working memory and top-down attention processes, we trained macaques to match two patterns presented with a delay between them. As the above processes are likely to require communication between brain regions, and the parietal cortex is involved in spatial attention, we simultaneously recorded neuronal activities from the interconnected parietal and middle temporal areas. We found that mnemonic information about the first pattern was retained in coherent oscillating activity between the areas in high-frequency bands, followed by coherent activity in low-frequency bands that mediate top-down attention on the relevant location.ONE SENTENCE SUMMARYGamma coherence allows retaining object features in a saliency map while lower frequency coherence facilitates attention.

scholarly journals Well-scale demonstration of distributed pressure sensing using fiber-optic DAS and DTS

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gerald K. Ekechukwu ◽  
Jyotsna Sharma
Keyword(s):  
Pressure Measurement ◽  
Mean Squared Error ◽  
Low Frequency ◽  
Coefficient Of Determination ◽  
Frequency Bands ◽  
Measurement Capability ◽  
Deep Well ◽  
Depth Analysis ◽  
Frequency Components ◽  
Distributed Pressure

AbstractIn this study, we used data from optical fiber-based Distributed Acoustic Sensor (DAS) and Distributed Temperature Sensor (DTS) to estimate pressure along the fiber. A machine learning workflow was developed and demonstrated using experimental datasets from gas–water flow tests conducted in a 5163-ft deep well instrumented with DAS, DTS, and four downhole pressure gauges. The workflow is successfully demonstrated on two experimental datasets, corresponding to different gas injection volumes, backpressure, injection methods, and water circulation rates. The workflow utilizes the random forest algorithm and involves a two-step process for distributed pressure prediction. In the first step, single-depth predictive modeling is performed to explore the underlying relationship between the DAS (in seven different frequency bands), DTS, and the gauge pressures at the four downhole locations. The single-depth analysis showed that the low-frequency components (< 2 Hz) of the DAS data, when combined with DTS, consistently demonstrate a superior capability in predicting pressure as compared to the higher frequency bands for both the datasets achieving an average coefficient of determination (or R2) of 0.96. This can be explained by the unique characteristic of low-frequency DAS which is sensitive to both the strain and temperature perturbations. In the second step, the DTS and the low-frequency DAS data from two gauge locations were used to predict pressures at different depths. The distributed pressure modeling achieved an average R2 of 0.95 and an average root mean squared error (RMSE) of 24 psi for the two datasets across the depths analyzed, demonstrating the distributed pressure measurement capability using the proposed workflow. A majority of the current DAS applications rely on the higher frequency components. This study presents a novel application of the low-frequency DAS combined with DTS for distributed pressure measurement.

scholarly journals Rest-fMRI Based Comparison Study between Autism Spectrum Disorder and Typically Control Using Graph Frequency Bands

2021 ◽  
Author(s):  
Alireza Talesh Jafadideh ◽  
Babak Mohammadzadeh Asl
Keyword(s):  
Autism Spectrum Disorder ◽  
Signal Processing ◽  
Default Mode Network ◽  
High Frequency ◽  
Low Frequency ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Default Mode ◽  
Frequency Bands ◽  
The Brain

AbstractGraph signal processing is a subset of signal processing enabling the analysis of functional magnetic resonance imaging (fMRI) data in brain topological domain. One of the most important and highly interested tool of GSP is graph Fourier transform (GFT) by which brain signals can be analyzed in different graph frequency bands. In this paper, the resting-state fMRI (rfMRI) data is analyzed using GFT tool in order to discover new knowledge about the autism spectrum disorder (ASD) and find features discriminating between ASD and typically control (TC) subjects. For ASD group, the signal concentration in both low and high frequency bands is decreased by increasing the age in most of the brain well-known networks. The ASD in comparison to TC shows less intention for changing the signal concentration level when the level is very low or very high. In graph low frequency band, increasing the age is along with increasing the segregation and integration of brain ROIs respectively for ASD and TC. Also, in this band, the brain ROIs integration of ASD is more than TC. By increasing the age, the auditory network of ASD subjects shows increasing segregation and integration in graph low and high frequency bands, respectively. The reduced segregation of default mode network in ASD is happened in graph middle and higher frequency bands. The functional connectivity analysis between low and high frequency signals shows that some of the high frequency ROIs have connections with all low frequency ROIs so that the most of these connections are dramatically and significantly different between ASD and TC. By analyzing the local vertex frequency spectrum (LVFS) of ASD and TC at different states, it is seen these groups show contradictory behaviors with respect to each other in brain default mode network in two states. The results of different scenarios at different graph frequency bands demonstrate that using functional and structural data together can provide powerful tool for recognizing the ASD or even other brain disorders.

scholarly journals VARIATION OF INSTANTANEOUS SPECTRAL CENTROID ACROSS BANDS OF SURFACE ELECTROMYOGRAPHIC SIGNALS

2021 ◽  
Vol 57 (2) ◽  
pp. 356-360
Author(s):  
Divya Bharathi Krishnamani ◽  
◽  
P. A. Karthick ◽  
Ramakrishnan Swaminathan ◽  
◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Frequency Band ◽  
Biceps Brachii ◽  
Shape Representation ◽  
Low Frequency ◽  
Geometric Feature ◽  
Frequency Bands ◽  
Spectral Centroid ◽  
Frequency Components ◽  
Semg Signals

Surface electromyography (sEMG) is a technique which noninvasively acquires the electrical activity of muscles and is widely used for muscle fatigue assessment. This study attempts to characterize the dynamic muscle fatiguing contractions with frequency bands of sEMG signals and a geometric feature namely the instantaneous spectral centroid (ISC). The sEMG signals are acquired from biceps brachii muscle of fifty-eight healthy volunteers. The frequency components of the signals are divided into low frequency band (10-45Hz), medium frequency band (55-95Hz) and high frequency band (95-400Hz). The signals associated with these bands are subjected to a Hilbert transform and analytical shape representation is obtained in the complex plane. The ISC feature is extracted from the resultant shape of the three frequency bands. The results show that this feature can differentiate the muscle nonfatigue and fatigue conditions (p<0.05). It is found the values of ISC is lower in fatigue conditions irrespective of frequency bands. It is also observed that the coefficient of variation of ISC in the low frequency band is less and it demonstrates the ability of handling inter-subject variations. Therefore, the proposed geometric feature from the low frequency band of sEMG signals could be considered for detecting muscle fatigue in various neuromuscular conditions.

Inaudible High-Frequency Sounds Affect Brain Activity: Hypersonic Effect

2000 ◽  
Vol 83 (6) ◽  
pp. 3548-3558 ◽  
Author(s):  
Tsutomu Oohashi ◽  
Emi Nishina ◽  
Manabu Honda ◽  
Yoshiharu Yonekura ◽  
Yoshitaka Fuwamoto ◽  
...  
Keyword(s):  
High Frequency ◽  
Brain Activity ◽  
Statistical Significance ◽  
Power Spectra ◽  
Low Frequency ◽  
Frequency Component ◽  
Frequency Range ◽  
Frequency Components ◽  
Left Thalamus ◽  
The Brain

Although it is generally accepted that humans cannot perceive sounds in the frequency range above 20 kHz, the question of whether the existence of such “inaudible” high-frequency components may affect the acoustic perception of audible sounds remains unanswered. In this study, we used noninvasive physiological measurements of brain responses to provide evidence that sounds containing high-frequency components (HFCs) above the audible range significantly affect the brain activity of listeners. We used the gamelan music of Bali, which is extremely rich in HFCs with a nonstationary structure, as a natural sound source, dividing it into two components: an audible low-frequency component (LFC) below 22 kHz and an HFC above 22 kHz. Brain electrical activity and regional cerebral blood flow (rCBF) were measured as markers of neuronal activity while subjects were exposed to sounds with various combinations of LFCs and HFCs. None of the subjects recognized the HFC as sound when it was presented alone. Nevertheless, the power spectra of the alpha frequency range of the spontaneous electroencephalogram (alpha-EEG) recorded from the occipital region increased with statistical significance when the subjects were exposed to sound containing both an HFC and an LFC, compared with an otherwise identical sound from which the HFC was removed (i.e., LFC alone). In contrast, compared with the baseline, no enhancement of alpha-EEG was evident when either an HFC or an LFC was presented separately. Positron emission tomography measurements revealed that, when an HFC and an LFC were presented together, the rCBF in the brain stem and the left thalamus increased significantly compared with a sound lacking the HFC above 22 kHz but that was otherwise identical. Simultaneous EEG measurements showed that the power of occipital alpha-EEGs correlated significantly with the rCBF in the left thalamus. Psychological evaluation indicated that the subjects felt the sound containing an HFC to be more pleasant than the same sound lacking an HFC. These results suggest the existence of a previously unrecognized response to complex sound containing particular types of high frequencies above the audible range. We term this phenomenon the “hypersonic effect.”

Simulations of high-resolution images of amorphous silicon films

1986 ◽  
Vol 44 ◽  
pp. 544-545
Author(s):  
G. Y. Fan ◽  
J. M. Cowley
Keyword(s):  
Electron Microscope ◽  
High Frequency ◽  
Fourier Transformation ◽  
Amorphous Materials ◽  
Low Frequency ◽  
Chromatic Aberration ◽  
Structure Information ◽  
Frequency Components ◽  
High Resolution Images ◽  
Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

ANALYSIS OF STAND RESEARCH RESULTS OF AXIAL DYNAMIC FORCE IN DRILLING BY THREE ROLLER CONE BIT

2019 ◽  
pp. 81-93
Author(s):  
В. М. Мойсишин ◽  
M. V. Lyskanych ◽  
R. A. Zhovniruk ◽  
Ye. P. Majkovych
Keyword(s):  
Low Frequency ◽  
Maximum Energy ◽  
Dynamic Force ◽  
Low Frequency Oscillation ◽  
Drilling Tool ◽  
Rock Destruction ◽  
Frequency Components ◽  
Harmonic Components ◽  
Experimental Values ◽  
Harmful Effects

The purpose of the proposed article is to establish the causes of oscillations of drilling tool and the basic laws of the distribution of the total energy of the process of changing the axial dynamic force over frequencies of spectrum. Variable factors during experiments on the classical plan were the rigidity of drilling tool and the hardness of the rock. According to the results of research, the main power of the process of change of axial dynamic force during drilling of three roller cone bits is in the frequency range 0-32 Hz in which three harmonic frequency components are allocated which correspond to the theoretical values of low-frequency and gear oscillations of the chisel and proper oscillations of the bit. The experimental values of frequencies of harmonic components of energy and normalized spectrum as well as the magnitude of the dispersion of the axial dynamic force and its normalized values at these frequencies are presented. It has been found that with decreasing rigidity of the drilling tool maximum energy of axial dynamic force moves from the low-frequency oscillation region to the tooth oscillation area, intensifying the process of rock destruction and, at the same time, protecting the tool from the harmful effects of the vibrations of the bit. Reducing the rigidity of the drilling tool protects the bit from the harmful effects of the vibrations generated by the stand. The energy reductions in these fluctuations range from 47 to 77%.

Changes in the Total Power of Beta and Theta Bands When Using EEG Biofeedback in Primary School-Age Children Having Difficulties with Voluntary Regulation

2020 ◽  
pp. 331-340
Author(s):  
Yuliya S. Dzhos ◽  
◽  
Irina A. Men’shikova ◽  
Keyword(s):  
Left Hemisphere ◽  
Spectral Characteristics ◽  
Low Frequency ◽  
School Age Children ◽  
Total Power ◽  
Eeg Biofeedback ◽  
Beta Band ◽  
Bioelectric Activity ◽  
Voluntary Regulation ◽  
The Brain

This article presents the results of the study on spectral electroencephalogram (EEG) characteristics in 7–10-year-old children (8 girls and 22 boys) having difficulties with voluntary regulation of activity after 10 and 20 neurofeedback sessions using beta-activating training. Brain bioelectric activity was recorded in 16 standard leads using the Neuron-Spectrum-4/VPM complex. The dynamics was assessed by EEG beta and theta bands during neurofeedback. An increase in the total power of beta band oscillations was established both after 10 and after 20 sessions of EEG biofeedback in the frontal (p ≤ 0.001), left parietal (p ≤ 0.036), and temporal (p ≤ 0.003) areas of the brain. A decrease in the spectral characteristics of theta band oscillations was detected: after 10 neurofeedback sessions in the frontal (p ≤ 0.008) and temporal (p ≤ 0.006) areas of both hemispheres, as well as in the parietal area of the left hemisphere (p ≤ 0.005); after 20 sessions, in the central (p ≤ 0.004), frontal (p ≤ 0.001) and temporal (p ≤ 0.001) areas of both hemispheres, as well as in the occipital (p ≤ 0.047) and parietal (p ≤ 0.001) areas of the left hemisphere. The study into the dynamics of bioelectric activity during biofeedback using EEG parameters in 7–10-year-old children with impaired voluntary regulation of higher mental functions allowed us to prove the advisability of 20 sessions, as the increase in high-frequency activity and decrease in low-frequency activity do not stop with the 10th session. Changes in these parameters after 10 EEG biofeedback sessions are expressed mainly in the frontotemporal areas of both hemispheres, while after a course of 20 sessions, in both the frontotemporal and central parietal areas of the brain.

A Novel Adaptive PET/CT Image Fusion Algorithm

2019 ◽  
Vol 14 (7) ◽  
pp. 658-666
Author(s):  
Kai-jian Xia ◽  
Jian-qiang Wang ◽  
Jian Cai
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
High Frequency ◽  
Malignant Tumors ◽  
Low Frequency ◽  
Combination Method ◽  
Ct Image ◽  
Fusion Algorithm ◽  
Pet Ct ◽  
Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

Sign in / Sign up

Export Citation Format

Share Document