Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 143
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 994
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3134
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
The aim of this study was to discriminate on a single-trial basis the cortical activity associated to two rates of torque development (RTDs) in imaginary isometric plantar flexions. Electroencephalographic (EEG), electrooculographic (EOG), and electromyographic (EMG) signals were recorded while ten healthy subjects imagined right-sided isometric ankle plantar-flexion tasks at moderate [from 0% to 60% of the maximal voluntary contraction (MVC) in 4 s] and ballistic (from 0% to 60% MVC as fast as possible) RTDs. The EEG signals were classified using feature extraction based on the marginal distribution of a discrete wavelet transform with optimization of the mother wavelet. The classifier was based on support vector machines (SVMs). Minimum misclassification rate for the best case was 8.3%. Average minimum misclassification rate over the ten subjects was (17.4 +/- 8.4)%. The two RTDs could be best differentiated from channel C4 on average. In conclusion, different RTDs could be differentiated in imaginary isometric plantar-flexion by only using cortical potentials recorded with surface EEG. This result constitutes the first step for the development of a new type of brain-computer interfaces that rely on kinetic parameters of a single limb rather than movements of opposite limbs.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1109/TBME.2008.2001139 | DOI Listing |
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