Deolindo Camila Sardeto, Ribeiro Mauricio Watanabe, Aratanha Maria Adelia, Afonso Rui Ferreira, Irrmischer Mona, Kozasa Elisa Harumi
Hospital Israelita Albert Einstein, São Paulo, Brazil.
Department of Integrative Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU Amsterdam, Amsterdam, Netherlands.
Front Syst Neurosci. 2020 Aug 7;14:53. doi: 10.3389/fnsys.2020.00053. eCollection 2020.
Meditation practices, originated from ancient traditions, have increasingly received attention due to their potential benefits to mental and physical health. The scientific community invests efforts into scrutinizing and quantifying the effects of these practices, especially on the brain. There are methodological challenges in describing the neural correlates of the subjective experience of meditation. We noticed, however, that technical considerations on signal processing also don't follow standardized approaches, which may hinder generalizations. Therefore, in this article, we discuss the usage of the electroencephalogram (EEG) as a tool to study meditation experiences in healthy individuals. We describe the main EEG signal processing techniques and how they have been translated to the meditation field until April 2020. Moreover, we examine in detail the limitations/assumptions of these techniques and highlight some good practices, further discussing how technical specifications may impact the interpretation of the outcomes. By shedding light on technical features, this article contributes to more rigorous approaches to evaluate the construct of meditation.
冥想练习起源于古代传统,因其对身心健康的潜在益处而越来越受到关注。科学界致力于仔细研究并量化这些练习的效果,尤其是对大脑的影响。在描述冥想主观体验的神经关联方面存在方法上的挑战。然而,我们注意到,信号处理方面的技术考量也未遵循标准化方法,这可能会阻碍研究结果的推广。因此,在本文中,我们讨论将脑电图(EEG)作为研究健康个体冥想体验的工具的用法。我们描述了主要的EEG信号处理技术以及截至2020年4月它们是如何被应用到冥想领域的。此外,我们详细审视了这些技术的局限性/假设,并强调了一些良好做法,进一步讨论了技术规格可能如何影响结果的解释。通过阐明技术特征,本文有助于采用更严谨的方法来评估冥想的构成。