Exploration of Brain Network Measures Across Three Meditation Traditions

Authors

  • Pankaj Pandey Indian Institute of Technology Gandhinagar
  • Pragati Gupta
  • Krishna Prasad Miyapuram

DOI:

https://doi.org/10.15540/nr.9.3.113

Keywords:

Meditation, Functional Connectivity, EEG signals, graph measures, support vector machine, machine learning, brainwaves, Himalayan Yoga, Isha Shoonya, Vipassana

Abstract

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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Published

2022-09-29

Issue

Vol. 9 No. 3 (2022)

Section

Research Papers

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