Multiway Array Decomposition of EEG Spectrum: Implications of Its Stability for the Exploration of Large-Scale Brain Networks

Multiway array decomposition methods have been shown to be promising statistical tools for identifying neural activity in the EEG spectrum. They blindly decompose the EEG spectrum into spatial-temporal-spectral patterns by taking into account inherent relationships among signals acquired at different frequencies and sensors. Our study evaluates the stability of spatial-temporal-spectral patterns derived by one particular method, parallel factor analysis (PARAFAC). We focused on patterns’ stability over time and in population and divided the complete data set containing data from 50 healthy subjects into several subsets. Our results suggest that the patterns are highly stable in time, as well as among different subgroups of subjects. Further, we show with simultaneously acquired fMRI data that power fluctuations of some patterns have stable correspondence to hemodynamic fluctuations in large-scale brain networks. We did not find such correspondence for power fluctuations in standard frequency bands, the common way of dealing with EEG data. Altogether, our results suggest that PARAFAC is a suitable method for research in the field of large-scale brain networks and their manifestation in EEG signal.

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A spectral perspective on the existence of hemispheric circulation regimes

10.5194/egusphere-egu21-13579 ◽

2021 ◽

Author(s):

Jacopo Riboldi ◽

Efi Rousi ◽

Fabio D'Andrea ◽

Gwendal Rivière ◽

François Lott

Keyword(s):

Rossby Wave ◽

General Circulation ◽

Large Scale ◽

Phase Speed ◽

General Circulation Models ◽

Wave Pattern ◽

Spectral Space ◽

Compact Representation ◽

Data Set ◽

Spectral Patterns

While the existence of regional weather regimes (e.g., over the North Atlantic) is a known result, the presence of preferred circulation patterns at the hemispheric scale is still disputed. Space-time spectral analysis can offer a different perspective to tackle this problem, as it provides a compact representation of the large-scale flow evolution. It can objectively extract the most relevant harmonics, in terms of spatial wavenumbers and temporal frequencies, that dominate the hemispheric Rossby wave pattern at a given time and is easily applicable to gridded data sets as Reanalysis or the output of general circulation models.With the aim to highlight the existence of clusters in the spectral space, we build a data set of spectra of upper-level meridional wind over midlatitudes (35&#176;N-75&#176;N) in the wavenumber/phase-speed domain for the 1979-2019 Reanalysis period. A spectrum is assigned to each day being located in the center of a sliding 61-days time window. This data set contains interesting information about the stationarity and the persistence of the hemispheric Rossby wave pattern. The most persistent harmonics are the ones related to quasi-stationary or westward propagating waves, as confirmed by an analysis of the dominant harmonics during atmospheric blocking events.Cluster analysis is performed using self-organizing maps (SOMs) on this data set. To assess its significance, the same procedure is applied to an artificially generated red noise with the same mean, variance and lag-1 covariance as the real data. This cross-check does not highlight a preferred number of circulation regimes in the spectral space. However, a subjective classification of the spectral patterns highlighted by the SOM analysis in four different groups can be attempted: 1) a ground state, with no particular deviation from climatology; 2) a state characterized by rapidly propagating, high wavenumber waves; 3) a state characterized by slowly propagating, low wavenumber waves; and 4) a state with a clear, dominant wavenumber. Spectral patterns corresponding to each of these groups are present regardless of the chosen number of SOMs.

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A New Methodology for Smoothing Power Peaks Produced by Electricity Demand and a Hydrokinetic Turbine for a Household Load on Grid Using Supercapacitors

World Electric Vehicle Journal ◽

10.3390/wevj12040235 ◽

2021 ◽

Vol 12 (4) ◽

pp. 235

Author(s):

Paul Arévalo ◽

Marcos Tostado-Véliz ◽

Francisco Jurado

Keyword(s):

Electric Vehicles ◽

Large Scale ◽

Data Sampling ◽

Energy Control ◽

Electrical Grid ◽

Hydrokinetic Turbine ◽

Power Fluctuations ◽

Sampling Intervals ◽

The Stability ◽

The Cost

The power fluctuations produced by electric vehicles represent a drawback in large-scale residential applications. In addition to that, short power peaks could pose a risk to the stability of the electrical grid. For this reason, this study presents a feasibility analysis for a residential system composed of electric vehicle chargers. The objective is focused on smoothing the power fluctuations produced by the charge by a supercapacitor through adequate energy control; in addition, self-consumption is analyzed. Data sampling intervals are also analyzed; the modeling was performed in Matlab software. The results show that there are errors of up to 9% if the data are measured at different sampling intervals. On the other hand, if the supercapacitor is considered, the system saves 59.87% of the energy purchased from the utility grid per day, and the self-consumption of electricity by prosumers can increase up to 73%. Finally, the hydrokinetic/supercapacitor/grid system would save up to 489.1 USD/year in the cost of purchasing electricity from the grid and would increase by 492.75 USD/year for the sale electricity.

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Prefrontal contributions to the stability and variability of thought and conscious experience

Neuropsychopharmacology ◽

10.1038/s41386-021-01147-7 ◽

2021 ◽

Author(s):

Andre Zamani ◽

Robin Carhart-Harris ◽

Kalina Christoff

Keyword(s):

Large Scale ◽

Conscious Experience ◽

Brain Networks ◽

Mental States ◽

Central Feature ◽

Theoretical Frameworks ◽

Dynamic Framework ◽

Prefrontal Function ◽

The Stability ◽

Directed Thought

AbstractThe human prefrontal cortex is a structurally and functionally heterogenous brain region, including multiple subregions that have been linked to different large-scale brain networks. It contributes to a broad range of mental phenomena, from goal-directed thought and executive functions to mind-wandering and psychedelic experience. Here we review what is known about the functions of different prefrontal subregions and their affiliations with large-scale brain networks to examine how they may differentially contribute to the diversity of mental phenomena associated with prefrontal function. An important dimension that distinguishes across different kinds of conscious experience is the stability or variability of mental states across time. This dimension is a central feature of two recently introduced theoretical frameworks—the dynamic framework of thought (DFT) and the relaxed beliefs under psychedelics (REBUS) model—that treat neurocognitive dynamics as central to understanding and distinguishing between different mental phenomena. Here, we bring these two frameworks together to provide a synthesis of how prefrontal subregions may differentially contribute to the stability and variability of thought and conscious experience. We close by considering future directions for this work.

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A Robust Approach to Variation in Carpathian Rusyn: Resampling-Based Methods for Small Data Sets

Journal of Linguistics/Jazykovedný casopis ◽

10.2478/jazcas-2021-0055 ◽

2021 ◽

Vol 72 (2) ◽

pp. 603-617

Author(s):

Moulay Zaidan Lahjouji-Seppälä ◽

Achim Rabus

Keyword(s):

Large Scale ◽

Small Data ◽

Data Sets ◽

Data Set ◽

Large Scale Data ◽

Dependent Variables ◽

Small Data Sets ◽

The Stability ◽

Sociolinguistic Status ◽

Scale Data

Abstract Quantitative, corpus based research on spontaneous spoken Carpathian Rusyn language can cause several data-related problems: Speakers are using ambivalent forms in different quantities, resulting in a biased data set – while a stricter data-cleaning process would lead to a large scale data loss. On top of that, polytomous categorical dependent variables are hard to analyze due to methodological limitations. This paper provides several approaches to face unbalanced and biased data sets containing variation of conjugational forms of the verb maty ‘to have’ and (po-)znaty ‘to know’ in Carpathian Rusyn language. Using resampling based methods like Cross-Validation, Bootstrapping and Random Forests, we provide a strategy for circumventing possible methodological pitfalls and gaining the most information from our precious data, without trying to p-hack the results. Calculating the predictive power of several sociolinguistic factors on linguistic variation, we can make valid statements about the (sociolinguistic) status of Rusyn and the stability of the old dialect continuum of Rusyn varieties.

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Machine Learning Verdict of EEG Signals in Brain Computer Interface

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1838114 ◽

2018 ◽

pp. 429-441

Author(s):

M. Jeyanthi ◽

C. Velayutham

Keyword(s):

Nearest Neighbor ◽

Technology Development ◽

Vital Role ◽

Svm Classifier ◽

K Nearest Neighbor ◽

Data Mining Technique ◽

Data Set ◽

Eeg Data ◽

Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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