scholarly journals Delta/theta band EEG differentially tracks low and high frequency speech-derived envelopes

2020 ◽  
Author(s):  
Felix Bröhl ◽  
Christoph Kayser
Keyword(s):  
High Frequency ◽  
Brain Activity ◽  
Low Frequency ◽  
List Type ◽  
Theta Band ◽  
Acoustic Fluctuations ◽  
Eeg Recordings ◽  
Band Limited ◽  
Speech Envelope ◽  
Speech Tracking

AbstractThe representation of speech in the brain is often examined by measuring the alignment of rhythmic brain activity to the speech envelope. To conveniently quantify this alignment (termed ‘speech tracking’) many studies consider the overall speech envelope, which combines acoustic fluctuations across the spectral range. Using EEG recordings, we show that using this overall envelope can provide a distorted picture on speech encoding. We systematically investigated the encoding of spectrally-limited speech-derived envelopes presented by individual and multiple noise carriers in the human brain. Tracking in the 1 to 6 Hz EEG bands differentially reflected low (0.2 – 0.83 kHz) and high (2.66 – 8 kHz) frequency speech-derived envelopes. This was independent of the specific carrier frequency but sensitive to attentional manipulations, and reflects the context-dependent emphasis of information from distinct spectral ranges of the speech envelope in low frequency brain activity. As low and high frequency speech envelopes relate to distinct phonemic features, our results suggest that functionally distinct processes contribute to speech tracking in the same EEG bands, and are easily confounded when considering the overall speech envelope.HighlightsDelta/theta band EEG tracks band-limited speech-derived envelopes similar to real speechLow and high frequency speech-derived envelopes are represented differentiallyHigh-frequency derived envelopes are more susceptible to attentional and contextual manipulationsDelta band tracking shifts towards low frequency derived envelopes with more acoustic detail

The Motion of Strawberry Leaves in an Air-Assisted Spray Field and its Influence on Droplet Deposition

2021 ◽  
Vol 64 (1) ◽  
pp. 83-93
Author(s):  
Shuo Wu ◽  
Jizhan Liu ◽  
Jiangshan Wang ◽  
Dianhe Hao ◽  
Rongkai Wang
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
High Amplitude ◽  
Azimuth Angle ◽  
List Type ◽  
Droplet Deposition ◽  
Inclination Angles ◽  
Turbulence Effect ◽  
Spray Field ◽  
Low Amplitude

HighlightsA visualization method for the motion of strawberry leaves in an air-assisted spray field is proposed.Strawberry leaves showed two motion states in different critical velocity ranges of the sprayer airflow.The airflow instability and the turbulence effect are considered important factors for the leaf vibrations.A strawberry leaf azimuth angle in the range of 90° to 270° can provide good deposition with smaller droplets.Abstract. The reasonable motion of crop plants in an air-assisted spray field can improve droplet deposition. Therefore, this study focuses on the motion of strawberry leaves and the droplet deposition mechanism in an air-assisted spray field. First, this study proposes a descriptive method for strawberry leaf motion in an air-assisted spray field and clarifies the important influence of strawberry leaf motion on droplet deposition. Second, an experiment was performed on the motion and droplet capture of single strawberry leaves in multi-position postures in an air-assisted spray field. The results showed that the leaves had two motion states (i.e., low amplitude with low frequency and high amplitude with high frequency) at different airflow velocities and inclination angles, and the critical airflow velocity corresponding to the two motion states was determined to be 8.7 m s-1. When the azimuth angle of the strawberry leaves is in the range of 90° to 270°, a reasonable inclination angle of the airflow and the high frequency and high amplitude vibration state of the leaves driven by the airflow will provide good deposition and canopy penetration of droplets with smaller diameters. Keywords: Air-assisted spray field, Droplet deposition, Motion, Spray, Strawberry leaves.

scholarly journals Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

2016 ◽  
Author(s):  
K. Kessler ◽  
R. A. Seymour ◽  
G. Rippon
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Autism Spectrum ◽  
List Type ◽  
Brain Oscillations ◽  
New Techniques ◽  
Low Frequencies ◽  
High Frequency Activity ◽  
Recent Classification ◽  
The Brain

AbstractAlthough atypical social behaviour remains a key characterisation of ASD, the presence of sensory and perceptual abnormalities has been given a more central role in recent classification changes. An understanding of the origins of such aberrations could thus prove a fruitful focus for ASD research. Early neurocognitive models of ASD suggested that the study of high frequency activity in the brain as a measure of cortical connectivity might provide the key to understanding the neural correlates of sensory and perceptual deviations in ASD. As our review shows, the findings from subsequent research have been inconsistent, with a lack of agreement about the nature of any high frequency disturbances in ASD brains. Based on the application of new techniques using more sophisticated measures of brain synchronisation, direction of information flow, and invoking the coupling between high and low frequency bands, we propose a framework which could reconcile apparently conflicting findings in this area and would be consistent both with emerging neurocognitive models of autism and with the heterogeneity of the condition.HighlightsSensory and perceptual aberrations are becoming a core feature of the ASD symptom prolife.Brain oscillations and functional connectivity are consistently affected in ASD.Relationships (coupling) between high and low frequencies are also deficient.Novel framework proposes the ASD brain is marked by local dysregulation and reduced top-down connectivityThe ASD brain’s ability to predict stimuli and events in the environment may be affectedThis may underlie perceptual sensitives and cascade into social processing deficits in ASD

Turbulent bremsstrahlung instability of whistler mode in the presence of enhanced ion-acoustic fluctuations

1984 ◽  
Vol 31 (3) ◽  
pp. 477-485 ◽  
Author(s):  
S. N. Sarma ◽  
M. Nambu ◽  
S. Bujarbarua
Keyword(s):  
Growth Rate ◽  
Electric Fields ◽  
High Frequency ◽  
Low Frequency ◽  
Resonant Interaction ◽  
Plasma Instability ◽  
Whistler Mode ◽  
Acoustic Fluctuations ◽  
Ion Acoustic ◽  
Nonlinear Force

In the presence of a low-frequency ion-acoustic turbulence and a high-frequency whistler-mode test wave, a new plasma instability occurs owing to a nonlinear force which originates from the resonant interaction between electrons and modulated nonlinear electric fields. The growth rate of the whistler mode is calculated and compared with observations.

scholarly journals SEREEGA: Simulating Event-Related EEG Activity

2018 ◽  
Author(s):  
Laurens R. Krol ◽  
Juliane Pawlitzki ◽  
Fabien Lotte ◽  
Klaus Gramann ◽  
Thorsten O. Zander
Keyword(s):  
Open Source ◽  
Brain Activity ◽  
Simulated Data ◽  
Ground Truth ◽  
List Type ◽  
Eeg Activity ◽  
Eeg Data ◽  
Different Types ◽  
Related Potentials ◽  
Eeg Recordings

AbstractElectroencephalography (EEG) is a popular method to monitor brain activity, but it can be difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings, ensuring that it is known beforehand which e ects are present in the data. As such, simulated data can be used, among other things, to assess or compare signal processing and machine learn-ing algorithms, to model EEG variabilities, and to design source reconstruction methods. In this paper, we present SEREEGA, short for Simulating Event-Related EEG Activity. SEREEGA is a MATLAB-based open-source toolbox dedicated to the generation of sim-ulated epochs of EEG data. It is modular and extensible, at initial release supporting ve different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general work ow of this toolbox, as well as a simulated data set demonstrating some of its functions.HighlightsSimulated EEG data has a known ground truth, which can be used to validate methods.We present a general-purpose open-source toolbox to simulate EEG data.It provides a single framework to simulate many different types of EEG recordings.It is modular, extensible, and already includes a number of head models and signals.It supports noise, oscillations, event-related potentials, connectivity, and more.

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 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 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 Bispectrum-based Cross-frequency Functional Connectivity: A Study of Alzheimer's Disease

2021 ◽  
Author(s):  
Dominik Klepl ◽  
Fei He ◽  
Min Wu ◽  
Daniel J Blackburn ◽  
Ptolemaios G Sarrigiannis
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
High Frequency ◽  
Neurodegenerative Disorder ◽  
Low Frequency ◽  
Brain Regions ◽  
Frequency Bands ◽  
Frequency Coupling ◽  
Eeg Recordings

Alzheimer's disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals such as electroencephalography (EEG) recordings into discrete frequency bands and analysing them in isolation from each other. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis approach, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. This work reports the first use of cross-bispectrum to reconstruct a cross-frequency FC network where each frequency band is treated as a layer in a multilayer network with both inter- and intra-layer edges. An increase of within-band FC in AD is observed in low-frequency bands using both methods. Bispectrum also detects multiple cross-frequency differences, mainly increased FC in AD in delta-theta coupling. An increased importance of low-frequency coupling and decreased importance of high-frequency coupling is observed in AD. Integration properties of AD networks are more vulnerable than HC, while the segregation property is maintained in AD. Moreover, the segregation property of γ is less vulnerable in AD, suggesting the shift of importance from high-frequency activity towards low-frequency components. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD. Moreover, the results demonstrate the advantages and limitations of using bispectrum to reconstruct FC networks.

scholarly journals Why did the animal turn? Time-varying step selection analysis for inference between observed turning points in high frequency data

2020 ◽  
Author(s):  
Rhys Munden ◽  
Luca Börger ◽  
Rory P. Wilson ◽  
James Redcliffe ◽  
Rowan Brown ◽  
...  
Keyword(s):  
High Frequency ◽  
Animal Movement ◽  
Turning Points ◽  
Low Frequency ◽  
Constant Time ◽  
Time Interval ◽  
List Type ◽  
Time Varying ◽  
Selection Analysis ◽  
Step Selection

AbstractStep selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as “selecting” each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions precisely at the times its locations are measured, if data are gathered at a relatively low frequency (every few minutes or hours) this is often the best that can be done. Nowadays, though, tracking data is increasingly gathered at very high frequencies, often ≥1Hz, so it may be possible to exploit these data to perform more behaviourally-meaningful step selection analysis.Here, we present a technique to do this. We first use an existing algorithm to determine the turning-points in an animal’s movement path. We define a “step” to be a straight-line movement between successive turning-points. We then construct a generalised version of integrated SSA (iSSA), called time-varying iSSA (tiSSA), which deals with the fact that turning-points are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and free-ranging goats (Capra aegagrus hircus), comparing our results to those of regular iSSA with locations that are separated by a constant time-interval.Using (regular) iSSA with constant time-steps can give results that are misleading compared to using tiSSA with the actual turns made by the animals. Furthermore, tiSSA can be used to infer covariates that are dependent on the time between turns, which is not possible with regular iSSA. As an example, we show that our study animals tend to spend less time between successive turns when the ground is rockier and/or the temperature is hotter.By constructing a step selection technique that works between observed turning-points of animals, we enable step selection to be used on high-frequency movement data, which are becoming increasingly prevalent in modern biologging studies. Furthermore, since turning-points can be viewed as decisions, our method places step selection analysis on a more behaviourally-meaningful footing compared to previous techniques.

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