Classifying EEG Signals of Mind-Wandering Across Different Styles of Meditation

Chaudhary, Shivam; Pandey, Pankaj; Miyapuram, Krishna Prasad; Lomas, Derek

doi:10.1007/978-3-031-15037-1_13

Cited by 15 publications

(3 citation statements)

References 24 publications

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“…Accordingly, such methods provide means to transform neuroimaging data into predictions of activated IA states and processes. In recent years, several pilot cross-sectional studies have demonstrated above chance decoding accuracies of internallydirected cognition and IA states in meditation based on EEG, MEG, and fMRI data (e.g., breath attention, mindwandering; Chaudhary et al, 2022;Pandey et al, 2023;Weng et al, 2020;Zhigalov et al, 2019). To the best of our knowledge, no study to date has used these formal reverse inference methods in a prospective mindfulness training study to test the impact of mindfulness training on IA during meditation.…”

Section: Brain Decoding Of Internal Attention: Formal Reverse Inferen...mentioning

confidence: 99%

The Mindfulness Internal Attention (MIA) Framework: Looking In to Understand Attentional Mechanisms of Mindfulness Training

Hadash¹,

Dar²,

Amir³

et al. 2023

Preprint

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Although attention is thought to have a definitive role in mindfulness meditation training and its salutary mechanisms of action, extant empirical evidence from more than two decades of research does not strongly support this central theoretical premise. We argue that the discrepancy between compelling theory and extant data is, in large part, explained by the field’s empirical focus on attention to external stimuli (e.g., visual) rather than attention to internal experience (e.g., bodily sensations, thoughts) that mindfulness training targets. To account for these findings, as well as to advance field-wide understanding of mindfulnesss mechanisms of action, we propose the Mindfulness Internal Attention (MIA) framework. We theorize that mindfulness meditation training mainly targets internal attentional processes. Moreover, we theorize that the effects of mindfulness training on internal attention generalize to late-stage external attentional processes that operate on abstracted representations of external information and share cognitive resources with internal attention. In contrast, we theorize that the effects of mindfulness training on internal attention will not generalize to early-stage external attentional processes that operate on sensory-perceptual information. We report a critical review of empirical findings that support these central tenets of the MIA framework. Finally, to advance research on mindfulness training and attention, we propose future directions related to the integration of emerging measures of internal attention into mindfulness research, study of shared resources between external and internal attention in mindfulness training, and study of internal attention processes as mechanisms of mindfulness training.

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Section: Brain Decoding Of Internal Attention: Formal Reverse Inferen...mentioning

confidence: 99%

The Mindfulness Internal Attention (MIA) Framework: Looking In to Understand Attentional Mechanisms of Mindfulness Training

Hadash¹,

Dar²,

Amir³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Machine learning classifiers are trained as the most practical method for spotting differences due to their strong pattern learning capabilities. Moreover, there has been a surge in studies using machine learning to categorize meditation states in recent years (Chaudhary et al, 2022;Pandey et al, 2022;Pandey & Miyapuram, 2020;Pandey & Miyapuram, 2021a, 2021c. Note.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Brain Network Measures Across Three Meditation Traditions

Pandey

Gupta²,

Miyapuram³

2022

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Research into the similarities and differences between various forms of meditation practice is still in its early stages. Here, utilizing functional connectivity and graph measures, we present our work examining three meditation traditions: Himalayan Yoga (HT), Isha Shoonya (SNY), and Vipassana (VIP). EEG activity of the meditative block is used to build functional brain connections to exploit the resulting networks between various meditation traditions and a control group. Support vector machine is employed for binary classification, and models are built with features generated via graph theory measures. We obtain maximum accuracy of 84.76% with gamma1, 90% with alpha, and 84.76% with theta in HT, SNY, and VIP, respectively. Our key findings involve (a) higher delta connectivity in Vipassana meditators, (b) synchronization of theta networks in the left hemisphere inspected to be stronger in the anterior frontal area across meditators, (c) greater involvement of gamma2 processing observed among Himalayan and Vipassana meditators, (d) increased left frontal activity contribution for all meditators in theta and gamma bands, and (e) modularity engaged extensively in gamma processing across all meditation traditions. Furthermore, we discuss the implication of this research for neurotechnology products to enable guided meditation among naive practitioners.

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“…In the same line, another recent study used machine learning to shown that the EEG correlates of moments of distraction during meditation practice can be identified across different meditation traditions. [5].…”

Section: Introductionmentioning

confidence: 99%

Detecting moments of distraction during meditation practice based on changes in the EEG signal

Pandey¹,

Rodriguez‐Larios²,

Miyapuram³

et al. 2022

Preprint

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<p>Electroencephalography (EEG) enables online monitoring brain activity, which can be used for neurofeedback. One of the growing applications of EEG neurofeedback is to facilitate meditation practice. Specifically, EEG neurofeedback can be used to alert participants whenever they get distracted during meditation practice based on changes in their brain activity. In this study, we develop machine learning models to detect moments of distraction (due to mind wandering or drowsiness) during meditation practice using EEG signals. We use EEG data of 24 participants while performing a breath focus meditation with experience sampling and extract twelve linear and non-linear EEG features. Features are fed to ten supervised machine learning models to classify (i) Breath Focus Awake (BFA) vs Breath Focus Sleepy (BFS), and (ii) BFA vs Mind Wandering (MW). We observe that the linear features achieve a maximum accuracy of 86% for classifying awake (BFA) and sleepy (BFS), whereas non-linear features have more predictive ability for classifying between BFA and MW with a maximum accuracy of nearly 78% . In addition, visualization of unsupervised t-SNE lower embeddings supports the evidence of distinct clusters for each condition. Overall our results show that machine learning algorithms can successfully identify periods of distraction during meditation practice in novice meditators based on linear and non-linear features of the EEG signal. Consequently, our results have important implications for the development of mobile EEG neurofeedback protocols aimed at facilitating meditation practice. </p>

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Classifying EEG Signals of Mind-Wandering Across Different Styles of Meditation

Cited by 15 publications

References 24 publications

The Mindfulness Internal Attention (MIA) Framework: Looking In to Understand Attentional Mechanisms of Mindfulness Training

The Mindfulness Internal Attention (MIA) Framework: Looking In to Understand Attentional Mechanisms of Mindfulness Training

Exploration of Brain Network Measures Across Three Meditation Traditions

Detecting moments of distraction during meditation practice based on changes in the EEG signal

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