EEG Spectral Changes Before and After an Eight-week Intervention Period of Preksha Meditation
November 10, 2016 DOI: 10.25148/etd.fidc001244 (opens in new tab) via OpenAlex
Summary
AI-generated from the abstractA study of Preksha meditation using electroencephalography (EEG) found that different forms of the meditation could not be clearly distinguished by brain activity. Thirteen novice meditators (10 females, 3 males; ages 19-49) had EEG data collected before and after an eight-week intervention (three sessions per week). A support vector machine algorithm classified brain activity among meditation forms with only 6-12% accuracy, indicating no clear EEG changes. The format of Preksha meditation studied did not elicit discernable brain activity differences using this method.
Study at a glance
| Characteristics | Pre-post intervention study |
|---|---|
| Sample size | 13 |
| Population | Novice meditators |
| Intervention | Preksha meditation |
| Duration | 8-week intervention, 3 sessions per week |
| Topics | Meditation |
| Keywords | Electroencephalography Audiology Pattern recognition psychology |
| Citations | 1 |
| Key finding | A support vector machine algorithm could not discriminate between different forms of Preksha meditation based on EEG spectral power, with classification accuracy of only 6-12%. |
Abstract
Various types of meditation techniques, primarily categorized into concentrative and mindfulness meditation, have evolved over the years to enhance the physiological and psychological well-being of people in all walks of life. However, the scientific knowledge of the impact of meditation on physiological and psychological well-being is very limited. Electroencephalography (EEG) was used to study the effect of a sequence of different forms of Preksha meditation on brain activity. EEG data from 13 novice participants (10 females, 3 males; Age: 19-49 yrs) were collected while meditating for the first time (pre) and at the end of an eight week (post) intervention period (3 meditation sessions/week). EEG spectral power densities were calculated in delta (1-4Hz), theta (4-8Hz), alpha (8-13Hz), beta (13-40Hz) and gamma (40-100Hz) bands. A Support vector machine algorithm based on the radial basis function kernel was used to classify different forms of Preksha meditation. The SVM classification was able to differentiate the brain activity amongst the forms of Preksha meditation with 6-12% accuracy only. These accuracies are extremely low and the classification was not able to discriminate between different forms of meditation within a session. It is therefore concluded, that the format of Preksha meditation utilized did not elicit clear changes in EEG, discernable using the SVM algorithm.