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Book ChapterDOI

Using EEG Data Analytics to Measure Meditation

TL;DR: This paper presents the study to detect “meditation” brain state by analyzing electroencephalographic (EEG) data, and found that overall Sample entropy is a good tool to extract information from EEG data.
Abstract: This paper presents the study we have done to detect “meditation” brain state by analyzing electroencephalographic (EEG) data. We firstly discuss what is “meditation” state and some prior studies on meditation. We then discuss how meditation state can be reflected in the subject’s brain waves; and what features of the brain waves data can be used in machine learning algorithms to classify meditation state from other states. We studied the suitability of 3 types of entropy: Shannon entropy, approximate entropy, and sample entropy in different circumstances. We found that overall Sample entropy is a good tool to extract information from EEG data. Discretization of EEG data enhances the classification rates by using both the approximate entropy and Shannon entropy.
Citations
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Book ChapterDOI
01 Jan 2019
TL;DR: A semi-automated approach is proposed by integrating the Shannon's entropy thresholding and DRLS-based segmentation procedure to extract the stained blood cell from digital PBC pictures.
Abstract: In recent years, a considerable number of approaches have been proposed by the researchers to evaluate infectious diseases by examining the digital images of peripheral blood cell (PBC) recorded using microscopes. In this chapter, a semi-automated approach is proposed by integrating the Shannon's entropy (SE) thresholding and DRLS-based segmentation procedure to extract the stained blood cell from digital PBC pictures. This work implements a two-step practice with cuckoo search (CS) and SE-based pre-processing and DRLS-based post-processing procedure to examine the PBC pictures. During the experimentation, the PBC pictures are adopted from the database leukocyte images for segmentation and classification (LISC). The proposed approach is implemented by considering the RGB scale and gray scale version of the PBC pictures, and the performance of the proposed approach is confirmed by computing the picture similarity and statistical measures computed with the extracted stained blood cell with the ground truth image.

14 citations


Cites background from "Using EEG Data Analytics to Measure..."

  • ...…bio-signal based approaches are limitedly considered to evaluate the disease in human body; further it is normally adopted to examine the abnormality arising in vital human organs, such as brain, heart, muscles, digestive system, etc (Lin and Li, 2017; Paramasivam et al., 2017; Ranjan et al. 2018)....

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Proceedings ArticleDOI
19 Jul 2020
TL;DR: The meditative state between expert and non-expert meditators is classified with 100% accuracy using d5,d6,d7,d8,a8 coefficients and Multi-Layer Perceptron and Quadratic Discriminant Analysis attain the highest accuracy.
Abstract: EEG oscillatory correlates of expert meditators have been studied in the time-frequency domain. Machine Learning techniques are required to expand the understanding of oscillatory signatures. In this work, we propose a methodological pipeline to develop machine learning models for the classification between expert and nonexpert meditative state. We carried out this study utilizing the online repository consisting of EEG dataset of 24 meditators that categorized as 12 experts and 12 nonexperts meditators. The pipeline consists of four stages that include feature engineering, machine learning classifiers, feature selection, and visualization. We decomposed signals using five wavelet families consisting of Haar, Biorthogonal(1.3-6.8), Daubechies( orders 2-10), Coiflet(orders 1-5), and Symlet(2-8), followed by feature extraction using relative entropy and power. We classified the meditative state between expert and non-expert meditators employing twelve classifiers to build machine learning models. Wavelet coefficients d8 shows the maximum classification accuracy in all the wavelet families. Wavelet orders Bior3.5 and Coif3 produce the maximum classification performance with the detail coefficient d8 using relative power. We have successfully classified the meditative state between expert and non-expert with 100% accuracy using d5,d6,d7,d8,a8 coefficients. Multi-Layer Perceptron and Quadratic Discriminant Analysis attain the highest accuracy. We have figured out the most discriminating channels during classification and reported 20 channels involving frontal, central and parietal regions. We plot the high dimensional structure of data by utilizing two feature reduction techniques PCA and t-SNE.

8 citations


Cites background from "Using EEG Data Analytics to Measure..."

  • ...A majority of research papers on EEG studies conclude cognitive aspects and lack exploration insights to provide a concrete methodology to analyze signals and develop classifying models of meditation [9], [10]....

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Proceedings ArticleDOI
06 Jul 2018
TL;DR: The proposed approach initially implements an amplitude based assessment to compute the peak-to-peak voltage value of the EEG signal, later, it implements time-frequency conversation procedure to transfer the signal into image based on the wavelet transform.
Abstract: Condition of brain can be examined using the brain-signals and brain-images. Signal based evaluation is simple and offers essential information compared with the image based methods. This paper proposes an approach to evaluate the benchmark EEG signals. The implemented approach initially implements an amplitude based assessment to compute the peak-to-peak voltage value of the EEG signal. Later, it implements time-frequency conversation procedure to transfer the signal into image based on the wavelet transform. Further, the S-transform approach is considered to extract the essential signal features for the classifier system. Firefly-Algorithm (FA) based approach is also considered to choose leading signal features considered to train and test the classifier unit. In this work, classifiers, such as Support-Vector-Machine (SVM), Random-Forest (RF) and K-Nearest Neighbor (KNN) are implemented and the result of this work offered an average accuracy of 80.39%. The works confirms that, proposed procedure offers better result on the chosen EEG signals.

7 citations


Cites background from "Using EEG Data Analytics to Measure..."

  • ...The brain bio-signals are mainly due to the electrical activity of the brain, which can be collected from the scalp section by using various electrodes [11]....

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Proceedings ArticleDOI
15 Nov 2019
TL;DR: A mechanism was designed that facilitates the patient to learn about the movement mechanisms and the way in which the movement interacts with the extremities of the upper and lower limbs and the patients demonstrated that the learning of The distribution of the limbs helps to improve your mobility and coordinate your movements.
Abstract: DOI: Graduated motor imagery is the technique used in rehabilitation therapies, for the treatment of chronic pain and in most cases for movement disorders, the technique consists in strategically presenting therapies that correspond to laterality perception, mirror therapies and motor imagery. In the present work a mechanism was designed that facilitates the patient to learn about the movement mechanisms and the way in which the movement interacts with the extremities of the upper and lower limbs, the design is based on being able to use 24 images of the upper limbs and lower, these images are grouped into two groups with a different background, one dark and the other light, these images are grouped and sequentially arranged in a video, each image has a duration of 2 seconds in the video, after this time the image, at the time each image is displayed, the patient raises his hand that corresponds to the image he is observing, if he observes an upper or lower limb that corresponds to the right side, the patient will raise his right hand, in order to eliminate any visual pollution that does not achieve concentration to the patient, the use of the virtual reality lens that is coupled is used side with a cellular equipment that is where the video can be observed, the proposal provides a brain computer interface with which the level of concentration and meditation of the patient is measured at the time of performing the technique. The use protocol consists of performing the exercise three times: in a first time the patient only observes the images in order to be able to identify them, in the second attempt the patient performs the exercise at the end of this, the results are indicated by identifying the successes and the failed ones, knowing these results, in the third attempt the patient makes the attempt again with the intention of improving the previous result. The evaluator will carry out the evaluation accompanied by a checklist to record the results. Finally, a small statistic is indicated to perform 10 patients who have been evaluated with the design, obtaining favorable results where it is appreciated that the longer the patient does not perform activities with his limbs, he loses the ability to recognize them, the patients demonstrated that the learning of The distribution of the limbs helps to improve your mobility and coordinate your movements.

3 citations

Proceedings ArticleDOI
15 Nov 2019
TL;DR: In the case of videogames, children develop high levels of concentration in almost the entire development of the game from the beginning to the end of thegame, compared to the levels that they develop when they do their homework, where children hardly concentrate having average records on average in the level of concentration and meditation.
Abstract: In the new times we are living, the use of technology is causing many of the customs are changing, one of them is the development of children, in previous years children used to play with their friends in the parks, technology It has caused many of them to perform recreation activities based on the use of video games, these games have caused children to develop certain skills and also change their behavior when they perform certain activities, in this work a comparison between the levels of concentration and meditation levels when children carry out activities related to the realization of academic activities related to the accomplishment of the school tasks that they must perform, with the levels of concentration and meditation when the child is doing activities related to video games, methodology that was used to evaluate the levels The concentration and meditation is based on the use of the Neurosky MindWave EEG device, which gives us the level of concentration and meditation that the person is on a scale of 0% to 100%, the results obtained show that In the case of videogames, children develop high levels of concentration in almost the entire development of the game from the beginning to the end of the game, compared to the levels that they develop when they do their homework, where children hardly concentrate having average records on average in the level of concentration and meditation.

2 citations

References
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Journal ArticleDOI
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
Abstract: In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM‘s. The approach is illustrated on a two-spiral benchmark classification problem.

8,811 citations

Journal ArticleDOI
TL;DR: Recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity are reviewed.
Abstract: The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.

6,135 citations

Journal ArticleDOI
TL;DR: The FACT-G meets or exceeds all requirements for use in oncology clinical trials, including ease of administration, brevity, reliability, validity, and responsiveness to clinical change.
Abstract: PURPOSEWe developed and validated a brief, yet sensitive, 33-item general cancer quality-of-life (QL) measure for evaluating patients receiving cancer treatment, called the Functional Assessment of Cancer Therapy (FACT) scale.METHODS AND RESULTSThe five-phase validation process involved 854 patients with cancer and 15 oncology specialists. The initial pool of 370 overlapping items for breast, lung, and colorectal cancer was generated by open-ended interview with patients experienced with the symptoms of cancer and oncology professionals. Using preselected criteria, items were reduced to a 38-item general version. Factor and scaling analyses of these 38 items on 545 patients with mixed cancer diagnoses resulted in the 28-item FACT-general (FACT-G, version 2). In addition to a total score, this version produces subscale scores for physical, functional, social, and emotional well-being, as well as satisfaction with the treatment relationship. Coefficients of reliability and validity were uniformly high. The ...

5,232 citations

Journal ArticleDOI
TL;DR: In this article, the authors evaluated four statistical models (Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS) for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model.
Abstract: The task of modeling the distribution of a large number of tree species under future climate scenarios presents unique challenges. First, the model must be robust enough to handle climate data outside the current range without producing unacceptable instability in the output. In addition, the technique should have automatic search mechanisms built in to select the most appropriate values for input model parameters for each species so that minimal effort is required when these parameters are fine-tuned for individual tree species. We evaluated four statistical models—Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS)—for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model. To test, we applied these techniques to four tree species common in the eastern United States: loblolly pine (Pinus taeda), sugar maple (Acer saccharum), American beech (Fagus grandifolia), and white oak (Quercus alba). When the four techniques were assessed with Kappa and fuzzy Kappa statistics, RF and BT were superior in reproducing current importance value (a measure of basal area in addition to abundance) distributions for the four tree species, as derived from approximately 100,000 USDA Forest Service’s Forest Inventory and Analysis plots. Future estimates of suitable habitat after climate change were visually more reasonable with BT and RF, with slightly better performance by RF as assessed by Kappa statistics, correlation estimates, and spatial distribution of importance values. Although RTA did not perform as well as BT and RF, it provided interpretive models for species whose distributions were captured well by our current set of predictors. MARS was adequate for predicting current distributions but unacceptable for future climate. We consider RTA, BT, and RF modeling approaches, especially when used together to take advantage of their individual strengths, to be robust for predictive mapping and recommend their inclusion in the ecological toolbox.

1,879 citations

Journal ArticleDOI
TL;DR: Sedimentation equilibrium of proteins, including membrane proteins and glycoproteins, is the method of choice for molar mass determinations and the study of self‐association and heterogeneous interactions, such as protein–protein, protein–nucleic acid, and protein–small molecule binding.
Abstract: Analytical ultracentrifugation (AU) is reemerging as a versatile tool for the study of proteins. Monitoring the sedimentation of macromolecules in the centrifugal field allows their hydrodynamic and thermodynamic characterization in solution, without interaction with any matrix or surface. The combination of new instrumentation and powerful computational software for data analysis has led to major advances in the characterization of proteins and protein complexes. The pace of new advancements makes it difficult for protein scientists to gain sufficient expertise to apply modern AU to their research problems. To address this problem, this review builds from the basic concepts to advanced approaches for the characterization of protein systems, and key computational and internet resources are provided. We will first explore the characterization of proteins by sedimentation velocity (SV). Determination of sedimentation coefficients allows for the modeling of the hydrodynamic shape of proteins and protein complexes. The computational treatment of SV data to resolve sedimenting components has been achieved. Hence, SV can be very useful in the identification of the oligomeric state and the stoichiometry of heterogeneous interactions. The second major part of the review covers sedimentation equilibrium (SE) of proteins, including membrane proteins and glycoproteins. This is the method of choice for molar mass determinations and the study of selfassociation and heterogeneous interactions, such as protein–protein, protein–nucleic acid, and protein–small molecule binding.

747 citations