Classification of meditation states through EEG: A method using discrete wavelet transform

Tee, Jen Looi; Phang, Swee King; Chew, Wei Jen; Phang, Siew Wei; Mun, Hou Kit

doi:10.1063/5.0001375

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Examining whether meditative theta is sensitive to change as a function of prolonged OM meditation training may be a foundational step in establishing a neural index of meditation quality (i.e., a measure that is sensitive to changes that occur acutely during a single session of meditation and over the course of longer-term meditation practice). Toward this end, employment of cutting-edge time-frequency 147 , source mapping 148 , and machine-learning 125,127 approaches appear particularly promising. Furthermore, it must be acknowledged that the current study involved an all-female sample, and therefore it is unknown to what extent the aforementioned considerations generalize to males.…”

Section: Resultsmentioning

confidence: 99%

An electrophysiological investigation on the emotion regulatory mechanisms of brief open monitoring meditation in novice non-meditators

Lin

Gloe

Louis

et al. 2020

Sci Rep

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Despite a growing literature supporting the salutary effects of mindfulness meditation on emotion regulation, the underlying mechanisms linking neural and subjective changes occurring during the actual practice of meditation with emotion regulatory effects observed after meditation remains virtually unexplored. the current study sought to address this gap in knowledge by testing the hypothesis that adoption of internally-directed focused attention, indexed by increased alpha and theta spectral power, during brief open monitoring (oM) mindfulness meditation predicts reduced emotional reactivity, as measured by the late positive potential (Lpp). Results revealed that the OM meditation did not produce demonstrable differences in alpha and theta power but did increase self-reported sleepiness relative to controls. follow-up analyses showed that sleepiness uniquely moderated the effect of meditation on the LPP, such that less sleepiness during meditation, but not the control audio, corresponded to smaller Lpps to negative images. change in theta, but not alpha power, between meditation and rest was positively correlated with the Lpp even after controlling for sleepiness. Although the primary hypothesis was unsupported, the findings demonstrate that phenomenological and neural changes occurring during oM meditation may modulate its subsequent "off-the-cushion" effects on emotional reactivity. Originating from a 2,500-year old Buddhist contemplative tradition, mindfulness has received increased interest from people around the world. Although definitions of mindfulness vary across time and context 1 , one of the most cited contemporary definitions of mindfulness refers to the adoption of a nonelaborative, nonjudgmental awareness to present-moment experience 2,3. Driving its rising popularity, a rapidly growing body of research has shown that adoption and training of mindfulness, and possessing higher dispositional levels of mindfulness, are related to a wide array of benefits 2-5. A frequently cited benefit of mindfulness involves its salutary effects on emotion regulation 6,7 , a core selfregulatory ability involving modulation of the generation, experience, and expression of emotion 8. Despite lay and scientific consensus that mindfulness promotes healthy emotion regulation, little is known about how mindfulness confers its emotion regulatory effects. Research into this question is complicated by three factors. First, emotion regulation is conceptualized as a complex dynamic process that unfolds over time 9,10. Consequently, mechanistic investigations of mindfulness-based emotion regulation may strongly benefit from employing methodologies with temporal sensitivity. Second, mindfulness is a multi-faceted construct differentiable as a state, trait, and mind-body training modality 11,12. Moreover, mindfulness as mind-body training is itself highly varied, ranging from traditional sitting meditations to a panoply of didactic exercises and integrative interventions 13,14. This construct heterogeneity complicates operationa...

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Section: Resultsmentioning

confidence: 99%

An electrophysiological investigation on the emotion regulatory mechanisms of brief open monitoring meditation in novice non-meditators

Lin

Gloe

Louis

et al. 2020

Sci Rep

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“…Traditional machine learning techniques have been extensively applied for many different meditation styles using various feature extraction methods. Features used for meditation state recognition are from either frequency domain such as Fourier transform and time-frequency analysis, or spatial-temporal domain such as linear analysis using independent component analysis (ICA), common spatial patterns (CSP), and linear discriminator (LD), as well as non-linear analysis using entropy, correlation dimension (CD), largest Lyapunov exponent (LLE), and hurst exponent (HE) ( Goshvarpour and Goshvarpour, 2012 ; Lin and Li, 2017 ; Han et al, 2020 ; Tee et al, 2020 ; Huang et al, 2021 ; Kora et al, 2021 ; Panachakel et al, 2021b ). Brain connectivity features have also been exploited ( Dissanayaka et al, 2015 ; Pandey et al, 2021 ).…”

Section: Introductionmentioning

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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“…Ahani et al [ 9 ] has classified mindfulness meditation EEG data using Stockwell transform and support vector machine with an accuracy of 85.0%. At the same time, Tee et al [ 66 ] achieved a classification accuracy of 96.9% for theta healing meditation using discrete wavelet transform and logistic regression. However, for LKM such results have not been reported so far.…”

Section: Introductionmentioning

confidence: 99%

Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions

Liyanagedera,

Hussain,

Singh

et al. 2023

Brain Inf.

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While a very few studies have been conducted on classifying loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data for a single session, there are no such studies conducted for multiple session EEG data. Thus, this study aims at classifying existing raw EEG meditation data on single and multiple sessions to come up with meaningful inferences which will be highly beneficial when developing algorithms that can support meditation practices. In this analysis, data have been collected on Pre-Resting (before-meditation), Post-Resting (after-meditation), LKM-Self and LKM-Others for 32 participants and hence allowing us to conduct six pairwise comparisons for the four mind tasks. Common Spatial Patterns (CSP) is a feature extraction method widely used in motor imaginary brain computer interface (BCI), but not in meditation EEG data. Therefore, using CSP in extracting features from meditation EEG data and classifying meditation/non-meditation instances, particularly for multiple sessions will create a new path in future meditation EEG research. The classification was done using Linear Discriminant Analysis (LDA) where both meditation techniques (LKM-Self and LKM-Others) were compared with Pre-Resting and Post-Resting instances. The results show that for a single session of 32 participants, around 99.5% accuracy was obtained for classifying meditation/Pre-Resting instances. For the 15 participants when using five sessions of EEG data, around 83.6% accuracy was obtained for classifying meditation/Pre-Resting instances. The results demonstrate the ability to classify meditation/Pre-Resting data. Most importantly, this classification is possible for multiple session data as well. In addition to this, when comparing the classification accuracies of the six mind task pairs; LKM-Self, LKM-Others and Post-Resting produced relatively lower accuracies among them than the accuracies obtained for classifying Pre-Resting with the other three. This indicates that Pre-Resting has some features giving a better classification indicating that it is different from the other three mind tasks.

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Classification of meditation states through EEG: A method using discrete wavelet transform

Cited by 7 publications

References 11 publications

An electrophysiological investigation on the emotion regulatory mechanisms of brief open monitoring meditation in novice non-meditators

An electrophysiological investigation on the emotion regulatory mechanisms of brief open monitoring meditation in novice non-meditators

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

Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions

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