EEG signal based classification before and after combined Yoga and Sudarshan Kriya

Sharma, Himika; Raj, Rajnish; Juneja, Mamta

doi:10.1016/j.neulet.2019.134300

Cited by 23 publications

(13 citation statements)

References 28 publications

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“…For example, Hinterberger et al ( 2011 ) used a linear classifier and could classify subjects in distinct pre-induced meditation states, while Ahani et al ( 2013 , 2014 ) using the Support Vector Machine (SVM) algorithm, associated EEG with respiration to discriminate whether stressed subjects engage or not in a 6-weeks intervention. In addition, Lee et al ( 2017 ) also used the SVM and an artificial neuronal network (ANN) based on spectral features to classify meditation expertise of focused breathing practitioners (CDM-FA), and Sharma et al ( 2019 ) employed ANN to differentiate meditators and non-meditators.…”

Section: Discussionmentioning

confidence: 99%

A Critical Analysis on Characterizing the Meditation Experience Through the Electroencephalogram

Deolindo

Ribeiro

Aratanha

et al. 2020

Front. Syst. Neurosci.

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Meditation practices, originated from ancient traditions, have increasingly received attention due to their potential benefits to mental and physical health. The scientific community invests efforts into scrutinizing and quantifying the effects of these practices, especially on the brain. There are methodological challenges in describing the neural correlates of the subjective experience of meditation. We noticed, however, that technical considerations on signal processing also don't follow standardized approaches, which may hinder generalizations. Therefore, in this article, we discuss the usage of the electroencephalogram (EEG) as a tool to study meditation experiences in healthy individuals. We describe the main EEG signal processing techniques and how they have been translated to the meditation field until April 2020. Moreover, we examine in detail the limitations/assumptions of these techniques and highlight some good practices, further discussing how technical specifications may impact the interpretation of the outcomes. By shedding light on technical features, this article contributes to more rigorous approaches to evaluate the construct of meditation.

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Section: Discussionmentioning

confidence: 99%

A Critical Analysis on Characterizing the Meditation Experience Through the Electroencephalogram

Deolindo

Ribeiro

Aratanha

et al. 2020

Front. Syst. Neurosci.

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“…The experimental group showed an improvement in performance regarding the increased beta band complexity for non-linear features, which implies the effective use of cognitive resources during the task (Daniel et al, 2022). Sharma et al (2019) tested the effects of Sudarshan Kriya Yoga on the EEG and came to the same conclusion, indicating that the SKY breathing technique could possibly be used in clinical practice. Their study aimed to evaluate the effects of SKY on the human brain by means of EEG signals.…”

Section: Discussionmentioning

confidence: 63%

“…Pre-and post-electroencephalogram (EEG) signals were collected from control and test groups before and after 3 months of regular SKY practice. Their results showed that the values of variance, standard deviation, zero crossing, and maximum and minimum of EEG signals increased in the study group but decreased in the control group (Sharma et al, 2019). The study by Srinivasan and Baijal (2007) examined the effects of SKY practice on the Mismatch Negativity Paradigm (MMN), an indicator of preattentive processing.…”

Section: Discussionmentioning

confidence: 99%

Quantitative electroencephalography interpretation of human brain activity after COVID-19 before and after Sudarshan Kriya Yoga

Kopańska

Kuduk

Łagowska

et al. 2022

Front. Hum. Neurosci.

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The COVID-19 pandemic has affected the entire world. The SARS-CoV-2 virus is wreaking havoc globally, leading to serious health problems and even death. The purpose of this study is to present the brainwave variability pattern using QEEG after exposure to COVID-19 and to introduce the subject of the Sudarshan Kriya Yoga (SKY)-based breathing technique. QEEG is one of the basic neurological examinations through which we can compare the changes in the nervous system after SARS-CoV-2 virus infection and observe the variation of brainwave frequencies with a breathing technique.

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“…Meditation expertise increases through training and practice, and there will be both state and trait effects which can be characterized in either behavioral data or brain activities such as resting EEG for trait effect and meditating EEG for state effect ( Cahn and Polich, 2006 ; Zarka et al, 2022 ). There is lack of studies on classification of brain states for different training and practicing stages longitudinally for individual meditation practitioners, but some efforts have been spent on classifying subjects with different levels of meditation expertise using EEG in either the meditating states ( Shaw and Routray, 2016 ; Lee et al, 2017 ; Pandey and Miyapuram, 2021 ) or the resting states ( Sharma et al, 2019 ), as detailed in Table 6 , where the results of our study in this paper on meditation stage classification using meditation EEG (MBSR1/MBSR2) and resting state EEG (REST1/REST2), are also presented for ease of comparison.…”

Section: Discussionmentioning

confidence: 99%

“…For meditation state/experience classification, the intra-subject classification strategy has been used in Panachakel et al (2021b) , the mix-subject classification strategy has been used in Ahani et al (2014) , Khoury et al (2015) , Shaw and Routray (2016) , Lee et al (2017) , Sharma et al (2019) , and Han et al (2020) , and the inter-subject classification strategy has been used in Panachakel et al (2021a , b) and Pandey and Miyapuram (2021) . The inter-subject classification strategy is most suitable for the general case but it is the most difficult due to inter-subject variation.…”

Section: Discussionmentioning

confidence: 99%

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

Shang,

Duan,

et al. 2023

Front. Hum. Neurosci.

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IntroductionThis study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP).MethodsWe investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario.ResultsFor MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively.ConclusionDeep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.

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EEG signal based classification before and after combined Yoga and Sudarshan Kriya

Cited by 23 publications

References 28 publications

A Critical Analysis on Characterizing the Meditation Experience Through the Electroencephalogram

A Critical Analysis on Characterizing the Meditation Experience Through the Electroencephalogram

Quantitative electroencephalography interpretation of human brain activity after COVID-19 before and after Sudarshan Kriya Yoga

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

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