Convolutional neural networks for classifying healthy individuals practicing or not practicing meditation according to the EEG data.
X Fu, Sergey Tamozhnikov, Alexander Saprygin, N A Istomina, D I Klemeshova, Alexander N. Savostyanov
Vavilovskii zhurnal genetiki i selektsii December 1, 2023 DOI: 10.18699/VJGB-23-98 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Cross-sectional study Peer reviewed |
|---|---|
| Sample size | 100 |
| Population | People who practice meditation and those who do not |
| Intervention | meditation |
| Topics | Meditation |
| Keywords | EEG Convolutional neural networks Event-related brain potentials Stop-signal paradigm |
| Citations | 4 |
| Key findings | A convolutional neural network using event-related potentials from a stop-signal paradigm classified meditators and non-meditators with 82% accuracy. |
Abstract
The development of objective methods for assessing stress levels is an important task of applied neuroscience. Analysis of EEG recorded as part of a behavioral self-control program can serve as the basis for the development of test methods that allow classifying people by stress level. It is well known that participation in meditation practices leads to the development of skills of voluntary self-control over the individual's mental state due to an increased concentration of attention to themselves. As a consequence of meditation practices, participants can reduce overall anxiety and stress levels. The aim of our study was to develop, train and test a convolutional neural network capable of classifying individuals into groups of practitioners and non-practitioners of meditation by analysis of eventrelated brain potentials recorded during stop-signal paradigm. Four non-deep convolutional network architectures were developed, trained and tested on samples of 100 people (51 meditators and 49 non-meditators). Subsequently, all structures were additionally tested on an independent sample of 25 people. It was found that a structure using a one-dimensional convolutional layer combining the layer and a two-layer fully connected network showed the best performance in simulation tests. However, this model was often subject to overfitting due to the limitation of the display size of the data set. The phenomenon of overfitting was mitigated by changing the structure and scale of the model, initialization network parameters, regularization, random deactivation (dropout) and hyperparameters of cross-validation screening. The resulting model showed 82 % accuracy in classifying people into subgroups. The use of such models can be expected to be effective in assessing stress levels and inclination to anxiety and depression disorders in other groups of subjects.
Comparable studies
Other cross-sectional and survey studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Construct Validity of the Five Facet Mindfulness Questionnaire in Meditating and Nonmeditating Samples Experienced meditators and nonmeditating comparison groups | 2008 | Cross-sectional study | |
| Neural Correlates of Dispositional Mindfulness During Affect Labeling Adult participants | 2007 | Observational cross-sectional | n = 27 |
| Assessing mindfulness in children and adolescents: Development and validation of the Child and Adolescent Mindfulness Measure (CAMM). School-aged children and adolescents | 2011 | Cross-sectional validation study | n = 1,413 |
| Mindfulness and self-compassion as predictors of psychological wellbeing in long-term meditators and matched nonmeditators Experienced meditators and demographically matched nonmeditators | 2012 | Cross-sectional study | n = 152 |
| Mechanisms of Action in the Inverse Relationship Between Mindfulness and Psychological Distress Undergraduates | 2008 | Cross-sectional, correlational |
Citations in the library
Cites 4
- The Effect of Meditation on Comprehension of Statements About One-Self and Others: A Pilot ERP and Behavioral Study. Frontiers in Human Neuroscience January 9, 2020
- Event-related potential correlates of mindfulness meditation competence. Neuroscience April 21, 2016
- Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings. Clinical Psychology Review December 1, 2010
- Impact of regular meditation practice on EEG activity at rest and during evoked negative emotions. International Journal of Neuroscience June 2005