Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
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
This paper presents a setup for the real-time extraction of Electroencephalography (EEG) and Electrocardiogram (ECG) features indicating the level of focus, relaxation, or meditation of a given subject. An algorithm for detecting meditation in real-time using the extracted ECG features is designed and shown to lead to accurate results using an online ECG measurement dataset. Similar methods can be used for EEG data, such that the proposed measurement setup can be used, for example, for investigating the effect of virtual reality based EEG training, with and without neurofeedback, on the capability of subjects to focus, relax, or meditate.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1109/EMBC.2019.8857832 | DOI Listing |
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