A Retrospective Analysis of Three Focused Attention Meditation Techniques: Mantra, Breath, and External-Point Meditation

Abstract

Objective The goal of this study is to compare the effectiveness of three different meditation techniques (two internal focus techniques and one external focus technique) using a low-cost portable electroencephalography (EEG) device, namely, MUSE, for an objective comparison. Methods This is an IRB-approved retrospective study. All participants in the study were healthy adults. Each study participant (n = 34) was instructed to participate in three meditation sessions: mantra (internal), breath (internal), and external point. The MUSE brain-sensing headband (EEG) was used to document the “total time spent in the calm state” and the “total time spent in the calm or neutral state” (outcomes) in each three-minute session to conduct separate analyses for the meditation type. Separate generalized linear models (GLM) with unstructured covariance structures were used to examine the association between each outcome and the explanatory variable (meditation type). For all models, if there was a significant association between the outcome and the explanatory variable, pairwise comparisons were carried out using the Tukey-Kramer correction. Results The median time (in seconds) spent in the calm state while practicing mantra meditation was 131.5 (IQR: 94-168), while practicing breath meditation was 150 (IQR: 113-164), and while practicing external-point meditation was 100 (IQR: 62-126). Upon analysis, there was a significant association between the meditation type and the time spent in the calm state (p-value = 0.0006). Conclusion This is the first study comparing “internal” versus “external” meditation techniques using an objective measure. Our study shows the breath and mantra technique as superior to the external-point technique as regards time spent in the calm state. Additional research is needed using a combination of “EEG” and patient-reported surveys to compare various meditative practices. The findings from this study can help incorporate specific meditation practices in future mindfulness-based studies that are focused on healthcare settings and on impacting clinical outcomes, such as survival or disease outcomes.

Eliciting brain waves of people with cognitive impairment during meditation exercises using portable electroencephalography in a smart-home environment: a pilot study

“…In a recent study (Hunkin et al, 2021), a series of meditation exercises were implemented to explore the feasibility of the device to support mindfulness assessment. Another study compared three different meditation techniques in order to evaluate their effectiveness (Sharma et al, 2022). Also, mindfulness training with Muse was associated, through self-reported information and EEG derived indicators, with improvements in Obsessive Compulsive disorder (OCD) symptoms (Hawley et al, 2021).…”

A scalable and robust system for audience EEG recordings

“…In terms of recorded brain signal quality, MUSE has been shown to be able to capture Event Related Potentials (ERPs) [ 10 , 17 , 18 ]. It has also been shown to be able to capture various aspects of induced and intrinsic brain activity in various aspects of human behavior, such as Stress [ 19 , 20 ], Attention [ 21 ], Meditation [ 22 , 23 ], Mindfulness [ 24 , 25 ], Rapid diagnosis of stroke [ 26 ], Drowsiness [ 9 ], Emotion Classification [ 27 ] and Fatigue [ 17 , 28 ]. In a recent study MUSE headsets were used in a hyper-scanning study in an art installation [ 29 ], which was previously using EMOTIV EPOC headsets [ 14 ].…”

A scalable and robust system for Audience EEG recordings

“…In terms of recored brain signal quality, MUSE has been shown to be able to capture Event Related Potentials (ERPs) (Krigolson et al, 2021; Krigolson et al, 2017). It has also been shown to be able various aspects of induced and intrinsic brain activity in various aspects of human behavior, such Stress (Asif et al, 2019; Phutela et al, 2022), Attention(Vortmann et al, 2022), Meditation (Kim et al, 2022; Sharma et al, 2022), Mindfulness (Hawley et al, 2021; Hunkin et al, 2021), Rapid diagnosis of stroke (Wilkinson et al, 2020), Drowsiness (LaRocco et al, 2020), Emotion Classification (Raheel et al, 2019) and Fatigue(Krigolson et al, 2021; Ruyi et al, 2017). In a recent study MUSE headsets were used in a hyper-scanning study in an art installation (Chen et al, 2022), which was previously using EMOTIV EPOC headsets (Dikker et al, 2021).…”