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Hurst exponents and linear regression with an application to low-power beta characterization in meditation EEG.

Kang-Ming Chang, Pei-Chen Lo

American Journal of Electroneurodiagnostic Technology June 1, 2005 DOI: 10.1080/1086508x.2005.11079519 (opens in new tab)

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AI-extracted from the abstract
Characteristics Theoretical or philosophical paper Peer reviewed
Topics Meditation
Keywords Brain Humans Diagnosis, computer-assisted Beta rhythm Linear models Algorithms Pattern recognition, automated
Key points Proposes a lever-like EEG feature-extraction method based on the Hurst exponent and regression-fitting errors for identifying beta rhythms, and claims it is superior to most methods using time- and frequency-domain feature-extraction parameters for this purpose.

Abstract

A lever-like EEG feature-extraction method based on the Hurst exponent and regression-fitting errors is proposed for identifying beta rhythms. The proposed method is superior to most methods using the time- and frequency-domain feature extraction parameters for identifying beta rhythms.

Comparable studies

Other theoretical and historical papers on meditation, most cited first.

Study Year Design Participants
Mindfulness: A proposed operational definition. 2004 Theoretical or philosophical paper
Mechanisms of mindfulness 2005 Theoretical or philosophical paper
How Does Mindfulness Meditation Work? Proposing Mechanisms of Action From a Conceptual and Neural Perspective 2011 Theoretical or philosophical paper
Mindfulness-based cognitive therapy for depression 2006 Theoretical or philosophical paper
Self-awareness, self-regulation, and self-transcendence (S-ART): a framework for understanding the neurobiological mechanisms of mindfulness 2012 Theoretical or philosophical paper

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