scholarly journals Mining the Mind: Linear Discriminant Analysis of MEG Source Reconstruction Time Series Supports Dynamic Changes in Deep Brain Regions During Meditation Sessions

2021 ◽  
Author(s):  
Daniela Calvetti ◽  
Brian Johnson ◽  
Annalisa Pascarella ◽  
Francesca Pitolli ◽  
Erkki Somersalo ◽  
...  
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Longitudinal Studies ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Atlas ◽  
Meditation Practice ◽  
Linear Discriminant ◽  
The Brain

AbstractMeditation practices have been claimed to have a positive effect on the regulation of mood and emotions for quite some time by practitioners, and in recent times there has been a sustained effort to provide a more precise description of the influence of meditation on the human brain. Longitudinal studies have reported morphological changes in cortical thickness and volume in selected brain regions due to meditation practice, which is interpreted as an evidence its effectiveness beyond the subjective self reporting. Using magnetoencephalography (MEG) or electroencephalography to quantify the changes in brain activity during meditation practice represents a challenge, as no clear hypothesis about the spatial or temporal pattern of such changes is available to date. In this article we consider MEG data collected during meditation sessions of experienced Buddhist monks practicing focused attention (Samatha) and open monitoring (Vipassana) meditation, contrasted by resting state with eyes closed. The MEG data are first mapped to time series of brain activity averaged over brain regions corresponding to a standard Destrieux brain atlas. Next, by bootstrapping and spectral analysis, the data are mapped to matrices representing random samples of power spectral densities in $$\alpha$$ α , $$\beta$$ β , $$\gamma$$ γ , and $$\theta$$ θ frequency bands. We use linear discriminant analysis to demonstrate that the samples corresponding to different meditative or resting states contain enough fingerprints of the brain state to allow a separation between different states, and we identify the brain regions that appear to contribute to the separation. Our findings suggest that the cingulate cortex, insular cortex and some of the internal structures, most notably the accumbens, the caudate and the putamen nuclei, the thalamus and the amygdalae stand out as separating regions, which seems to correlate well with earlier findings based on longitudinal studies.

scholarly journals The Use of Satellite Information (MODIS/Aqua) for Phenological and Classification Analysis of Plant Communities

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 561 ◽  
Author(s):  
Yulia Ivanova ◽  
Anton Kovalev ◽  
Oleg Yakubailik ◽  
Vlad Soukhovolsky
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Plant Communities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Weather Conditions ◽  
Linear Discriminant ◽  
Canonical Correlations

Vegetation indices derived from remote sensing measurements are commonly used to describe and monitor vegetation. However, the same plant community can have a different NDVI (normalized difference vegetation index) depending on weather conditions, and this complicates classification of plant communities. The present study develops methods of classifying the types of plant communities based on long-term NDVI data (MODIS/Aqua). The number of variables is reduced by introducing two integrated parameters of the NDVI seasonal series, facilitating classification of the meadow, steppe, and forest plant communities in Siberia using linear discriminant analysis. The quality of classification conducted by using the markers characterizing NDVI dynamics during 2003–2017 varies between 94% (forest and steppe) and 68% (meadow and forest). In addition to determining phenological markers, canonical correlations have been calculated between the time series of the proposed markers and the time series of monthly average air temperatures. Based on this, each pixel with a definite plant composition can be characterized by only four values of canonical correlation coefficients over the entire period analyzed. By using canonical correlations between NDVI and weather parameters and employing linear discriminant analysis, one can obtain a highly accurate classification of the study plant communities.

Zur Evolution des deutschen Konjunkturzyklus 1958-2004 / On the Evolution of German Business Cycles 1958-2004

2008 ◽  
Vol 228 (1) ◽  
Author(s):  
Ullrich Heilemann ◽  
Roland Schuhr
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Business Cycles ◽  
Business Cycle ◽  
Linear Discriminant Analysis ◽  
Turning Point ◽  
Classification Methods ◽  
The West ◽  
Linear Discriminant ◽  
Macroeconomic Variables

SummaryThis paper examines changes of the (West) German business cycle from 1958 to 2004. It starts with a multivariate linear discriminant analysis (LDA) based decomposition of the cycle into 4 phases (upswing, upper turning point, downswing, lower turning point). After examining inter-cyclical changes of the cycle, i.e. changes of the weights of the 12 macroeconomic variables employed for classification, the question of intra-cyclical changes is addressed. This is done by using DLDA, a new dynamic variant of LDA which exploits the time series character of the data used to analyse changes of the multivariate structure of the cycle. The DLDA results exemplify that the transition from one to the next phase is much smoother and more continuous than might be expected. Within the sample examined these movements vary as well as the weights attributed to the classifying variables. In a methodological perspective DLDA turns out to be a promising broadening of classification methods.

The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis

NeuroImage ◽  
2016 ◽  
Vol 129 ◽  
pp. 279-291 ◽  
Author(s):  
Matthias S. Treder ◽  
Anne K. Porbadnigk ◽  
Forooz Shahbazi Avarvand ◽  
Klaus-Robert Müller ◽  
Benjamin Blankertz
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Optimal Estimation ◽  
Linear Discriminant

MULTIPLE CLASSIFICATION TECHNIQUES TOWARD A ROBUST AND RELIABLE P300 BCI SYSTEM

2020 ◽  
Vol 32 (02) ◽  
pp. 2050010
Author(s):  
Fatma EL-Zahraa M. Labib ◽  
Islam A. Fouad ◽  
Mai S. Mabrouk ◽  
Amr A. Sharawy
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Discriminant ◽  
The Brain ◽  
Bci System

A brain–computer interface (BCI) can be used for people with severe physical disabilities such as ALS or amyotrophic lateral sclerosis. BCI can allow these individuals to communicate again by creating a new communication channel directly from the brain to an output device. BCI technology can allow paralyzed people to share their intent with others, and thereby demonstrate that direct communication from the brain to the external world is possible and that it might serve useful functions. BCI systems include machine learning algorithms (MLAs). Their performance depends on the feature extraction and classification techniques employed. In this paper, we propose a system to exploit the P300 signal in the brain, a positive deflection in event-related potentials. The P300 signal can be incorporated into a spelling device. There are two benefits behind this kind of research. First of all, this work presents the research status and the advantages of communication via a BCI system, especially the P300 BCI system for disordered people, and the related literature review is presented. Secondly, the paper discusses the performance of different machine learning algorithms. Two different datasets are presented: the first dataset 2004 and the second dataset 2019. A preprocessing step is introduced to the subjects in both datasets first to extract the important features before applying the proposed machine learning methods: linear discriminant analysis (LDA I and LDA II), support vector machine (SVM I, SVM II, SVM III, and SVM IV), linear regression (LREG), Bayesian linear discriminant analysis (BLDA), and twin support vector machine (TSVM). By comparing the performance of the different machine learning systems, in the first dataset it is found that BLDA and SVMIV classifiers yield the highest performance for both subjects “A” and “B”. BLDA yields 98% and 66% for 15th and 5th sequences, respectively, whereas SVMIV yields 98% and 54.4% for 15th and 5th sequences, respectively. While in the second dataset, it is obvious that BLDA classifier yields the highest performance for both subjects “1” and “2”, it achieves 90.115%. The paper summarizes the P300 BCI system for the two introduced datasets. It discusses the proposed system, compares the classification methods performances, and considers some aspects for the future work to be handled. The results show high accuracy and less computational time which makes the system more applicable for online applications.

scholarly journals Predicting Aphasia from Strokes

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Joseph Jia ◽  
Joanna Gilberti
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Brain Lesion ◽  
Brain Cells ◽  
Lesion Volume ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Lesion Location ◽  
Predictive Rate ◽  
The Brain

Strokes can occur when someone’s blood vessels get blocked and the nutrients and oxygen being transported will not reach the brain. When a stroke happens, the brain cells don’t get the nutrients they need and start to die [3]. This could cause different side effects after stroke. In this study, we try to predict the possibility of one type of after-stroke side effect, aphasia, using Machine Learning (ML) techniques. Using the data of a study about brain lesion damage after a stroke and what effects the patients were experiencing afterward, we trained a model to predict whether a person may have aphasia based on where their lesion was, how big the lesion was, how long ago their stroke was, and some other factors. We evaluated several classification methods and found that using linear discriminant analysis was the most accurately predicting when we used age, sex, lesion location, lesion volume, and many more. By linear discriminant analysis, we were able to have a 91% overall predictive rate of patients having aphasia or not after experiencing a stroke.

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