Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 144
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 144
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 212
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3106
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Following investigations into the benefits of meditation on psychological health and well-being, research is now seeking to understand the mechanisms underlying these outcomes. This study aimed to identify natural alpha and theta frequency components during eyes-closed resting and concentrative meditation states and examined their differences within and between two testing sessions. Novice meditators had their EEG recorded during eyes-closed resting and concentrative meditation conditions, before and after engaging in a brief daily concentrative meditation practice for approximately one-month. Separate frequency Principal Components Analyses (f-PCA) yielded four spectral components of interest, congruent between both conditions and sessions: Delta-Theta-Alpha, Low Alpha, High Alpha, and Alpha-Beta. While all four components showed some increase in the meditation condition at the second session, only Low Alpha (∼9.5-10.0 Hz) showed similar increases while resting. These findings support the use of f-PCA as a novel method of data analysis in the investigation of psychophysiological states in meditation.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1177/15500594211065897 | DOI Listing |
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