Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

High-Level and Low-Level Awareness

High-Level and Low-Level Awareness

Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Exploring the mechanisms of biofield therapy through joint electrophysiological recordings in humans and mice.

IBRO neuroscience reports·2026

Same author

Prevalence and Predictors of Self-Reported Adverse Experiences in Digital Meditation Training: 2 Randomized Controlled Trials.

JMIR mental health·2026

Same author

Modality-specific predictive templates in pre-stimulus EEG activity.

Neuropsychologia·2026

Same author

The effects of intensive mindfulness meditation training on mental health: evidence of effectiveness and safety from a matched-controlled intervention study.

Cognitive behaviour therapy·2026

Same author

Genomic correlates of self-reported psychic experiences: An exploratory analysis.

Explore (New York, N.Y.)·2026

Same author

From simple mechanics to complex dynamics: A dynamical systems science of mindfulness and meditation.

The American psychologist·2025

Search research articles

Related Experiment Video

Updated: Jul 10, 2026

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During

Noga Aviad¹, Oz Moskovich², Ophir Orenstein²

¹Observing Minds Laboratory, School of Psychological Science, University of Haifa, Haifa, Israel.

Biological Psychiatry Global Open Science

|February 6, 2025

Summary

Machine learning accurately distinguished meditation from mind-wandering states using electroencephalography (EEG) data. This approach identified key EEG features, like high-frequency oscillations, characterizing focused attention meditation.

Keywords:

Complex systems EEG Machine learning Meditation Mindfulness

More Related Videos

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Published on: May 11, 2020

Related Experiment Videos

Last Updated: Jul 10, 2026

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Published on: May 11, 2020

Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Neuroelectrophysiological studies of mindfulness and meditation are rapidly growing.
Traditional analysis methods struggle with the complex, nonlinear nature of meditation's neurophysiology.
Understanding the brain's activity during meditation requires advanced analytical techniques.

Purpose of the Study:

To reveal the complex, systemic neuroelectrophysiology of meditation states.
To apply machine learning for analyzing electroencephalography (EEG) data during meditation.
To identify specific EEG features that characterize focused attention meditation.

Main Methods:

Applied an extreme gradient boosting classification algorithm to EEG data.
Utilized 4 complementary feature importance methods.
Recorded EEG from 26 experienced meditators during focused attention meditation and mind-wandering states.

Main Results:

The machine learning algorithm achieved 83% accuracy in classifying meditation versus mind-wandering states.
Area under the ROC curve was 79%, and F1 score was 74%.
Identified 10 EEG features, including increased high-frequency power and coherence, associated with meditation.

Conclusions:

The findings delineate the complex systemic oscillatory activity characterizing meditation.
Machine learning provides a powerful tool for analyzing neurophysiological data in meditation research.
Specific EEG patterns are linked to the focused attention state in experienced meditators.