Effect of mindfulness meditation on brain–computer interface performance

Tan, Lee-Fan; Dienes, Zoltán; Jansari, Ashok; Goh, Sing-Yau

doi:10.1016/j.concog.2013.10.010

Cited by 69 publications

(52 citation statements)

References 63 publications

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“…But there are other cases. For example, when a reviewer asks for more subjects to be run, and the original results were already tested at the 5% level, Bayesian statistics are needed (as in the Tan et al (2014) example above; orthodoxy is ruled out by its own logic in this case). Running a Bayes factor when non-significant results are obtained is simply a way that we as a community can come to know Bayes, and to obtain answers where we need answers, and none are forthcoming from orthodoxy.…”

Section: Resultsmentioning

confidence: 99%

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Using Bayes to get the most out of non-significant results

2014

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No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors.

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Section: Resultsmentioning

confidence: 99%

“…Tan et al (2009, 2014) tested people on their ability to use a Brain–Computer Interface (BCI). People were randomly assigned to three groups: no treatment, 12 weeks of mindfulness meditation, and an active control (12 weeks of learning to play the guitar).…”

Section: Examples Using the Different Ways Of Representing The Predicmentioning

confidence: 99%

Using Bayes to get the most out of non-significant results

2014

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“…in their experiment used healthy subjects as a comparator, and their conclusions supported the use of music-supported therapy for chronic stroke patients. While Tan L.F 28. researched only healthy participants, the music-supported therapy group also experienced the expected effects.…”

Section: Discussionmentioning

confidence: 94%

Improvement in Stroke-induced Motor Dysfunction by Music-supported Therapy: A Systematic Review and Meta-analysis

Zhang

Ren

et al. 2016

Sci Rep

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To conduct a meta-analysis of clinical trials that examined the effect of music-supported therapy on stroke-induced motor dysfunction, comprehensive literature searches of PubMed, Embase and the Cochrane Library from their inception to April 2016 were performed. A total of 10 studies (13 analyses, 358 subjects) were included; all had acceptable quality according to PEDro scale score. The baseline differences between the two groups were confirmed to be comparable. Compared with the control group, the standardized mean difference of 9-Hole Peg Test was 0.28 (−0.01, 0.57), 0.64 (0.31, 0.97) in Box and Block Test, 0.47 (0.08, 0.87) in Arm Paresis Score and 0.35 (−0.04, 0.75) in Action Research Arm Test for upper-limb motor function, 0.11 (−0.24, 0.46) in Berg Balance Scale score, 0.09 (−0.36, 0.54) in Fugl-Meyer Assessment score, 0.30 (−0.15, 0.74) in Wolf Motor Function Test, 0.30 (−0.15, 0.74) in Wolf Motor Function time, 0.65 (0.14, 1.16) in Stride length and 0.62 (0.01, 1.24) in Gait Velocity for total motor function, and 1.75 (0.94, 2.56) in Frontal Assessment Battery score for executive function. There was evidence of a positive effect of music-supported therapy, supporting its use for the treatment of stroke-induced motor dysfunction. This study was registered at PRESPERO (CRD42016037106).

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“…The SMR rhythm is generally largest over central scalp regions covering the sensorimotor cortex. Because there is a large body of literature showing that spiritual practice, such as praying and meditating, as well as mindfulness practice, are associated with brain plasticity (Lutz et al, 2004; Lazar et al, 2005; Hölzel et al, 2011; Lu et al, 2014; Tan L.- F. et al, 2014), we also investigated possible differences in brain structure between people praying frequently and people who do not. Furthermore, we focused on the question whether the individual ability to control one’s own brain activity is related to volumetric aspects of the brain.…”

Section: Introductionmentioning

confidence: 99%

Ability to Gain Control Over One’s Own Brain Activity and its Relation to Spiritual Practice: A Multimodal Imaging Study

Kober

Witte

Ninaus

et al. 2017

Front. Hum. Neurosci.

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Spiritual practice, such as prayer or meditation, is associated with focusing attention on internal states and self-awareness processes. As these cognitive control mechanisms presumably are also important for neurofeedback (NF), we investigated whether people who pray frequently (N = 20) show a higher ability of self-control over their own brain activity compared to a control group of individuals who rarely pray (N = 20). All participants underwent structural magnetic resonance imaging (MRI) and one session of sensorimotor rhythm (SMR, 12–15 Hz) based NF training. Individuals who reported a high frequency of prayer showed improved NF performance compared to individuals who reported a low frequency of prayer. The individual ability to control one’s own brain activity was related to volumetric aspects of the brain. In the low frequency of prayer group, gray matter volumes in the right insula and inferior frontal gyrus were positively associated with NF performance, supporting prior findings that more general self-control networks are involved in successful NF learning. In contrast, participants who prayed regularly showed a negative association between gray matter volume in the left medial orbitofrontal cortex (Brodmann’s area (BA) 10) and NF performance. Due to their regular spiritual practice, they might have been more skillful in gating incoming information provided by the NF system and avoiding task-irrelevant thoughts.

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Effect of mindfulness meditation on brain–computer interface performance

Cited by 69 publications

References 63 publications

Using Bayes to get the most out of non-significant results

Using Bayes to get the most out of non-significant results

Improvement in Stroke-induced Motor Dysfunction by Music-supported Therapy: A Systematic Review and Meta-analysis

Ability to Gain Control Over One’s Own Brain Activity and its Relation to Spiritual Practice: A Multimodal Imaging Study

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