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
The biomedical signal classification accuracy on motor imagery is not always satisfactory, partially because not all the important features have been effectively extracted. This paper proposes an improved dynamic feature extraction approach based on a time-frequency representation and an optimal sequence similarity measurement. Since the wavelet packet decomposition (WPD) generates more detailed signal variation information and the dynamic time warping (DTW) helps optimally measure the sequence similarity, more important features are kept for classification. We apply the extracted features from our proposed method to Electroencephalogram (EEG) based motor imagery through the OpenBCI device and obtain higher classification accuracy. Compared with traditional feature extraction methods, there is a significant classification accuracy improvement from 83.53% to 90.89%. Our work demonstrates the importance of the advanced feature extraction in time series data analysis, e.g. biomedical signal.
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Source |
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http://dx.doi.org/10.1109/EMBC.2018.8512914 | DOI Listing |
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