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This study introduces a new method using topological data analysis to analyze brain activity patterns in meditators. The findings reveal distinct topological features in electroencephalogram spectral powers between long-term meditators and naive practitioners.

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Area of Science:

  • Neuroscience
  • Data Science
  • Medical Imaging

Background:

  • Meditation offers health benefits, prompting neuroscientific research into its effects on brain activity.
  • Electroencephalogram (EEG) is a common tool for studying meditation's neuroplastic effects due to its cost-effectiveness and temporal resolution.
  • Standard analysis of EEG spectral power maps often uses statistical methods, potentially missing complex topological information.

Purpose of the Study:

  • To introduce a novel inference procedure using topological data analysis (TDA) for EEG signal processing.
  • To apply persistent homology to analyze topographic power maps derived from high-density EEG signals.
  • To compare the topological features of EEG spectral powers between long-term meditators and meditation-naive individuals.

Main Methods:

  • Utilized topological data analysis (TDA), specifically persistent homology, on EEG topographic power maps.
  • Developed a novel inference procedure based on sublevel set filtrations of power maps.
  • Applied the TDA pipeline to both simulated and real high-density EEG data.

Main Results:

  • The study successfully applied the novel TDA pipeline to analyze EEG data.
  • Distinct persistent homological features were observed in the high-frequency bands of EEG signals between meditators and non-meditators.
  • The findings suggest TDA can offer unique insights into meditation-induced brain activity changes.

Conclusions:

  • Topological data analysis, particularly persistent homology, provides a novel approach to understanding EEG spectral power variations.
  • The proposed method can differentiate between long-term meditators and meditation-naive practitioners based on their EEG topographic features.
  • This research highlights the potential of TDA in advancing neuroscientific investigations of meditation.