Severity: Warning
Message: fopen(/var/lib/php/sessions/ci_sessiondql9p7a5l10anj19jdkp89vcu5f5f81m): Failed to open stream: No space left on device
Filename: drivers/Session_files_driver.php
Line Number: 177
Backtrace:
File: /var/www/html/index.php
Line: 316
Function: require_once
Severity: Warning
Message: session_start(): Failed to read session data: user (path: /var/lib/php/sessions)
Filename: Session/Session.php
Line Number: 137
Backtrace:
File: /var/www/html/index.php
Line: 316
Function: require_once
Severity: Warning
Message: file_get_contents(https://...@remsenmedia.com&api_key=81853a771c3a3a2c6b2553a65bc33b056f08&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3145
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
Electroencephalography (EEG) signals are a valuable source of information for investigating brain activity and different types of brain-related disease diagnoses. However, EEG signals are often contaminated by various kinds of noises/artifacts. Several methods have been proposed for EEG reconstruction/denoising to facilitate signal analysis, but such algorithms often fail when the EEG contains extreme artifacts. This paper presents a novel method for reconstructing EEG signals using a variant of the variational autoencoder (VAE) called beta-VAE. Through extensive evaluation of our model on the DEAP dataset, we show that the β-VAE architecture learns a compressed representation of the EEG signal in an unsupervised manner, and the reconstructed signal contains less artifact. We compare our proposed method with different baselines and state-of-the-art techniques for EEG signal denoising, demonstrating significantly reduced reconstruction error under artificially induced noise. The results suggest that our approach has great potential for improving the analysis and understanding of EEG signals in clinical and research settings.
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Source |
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http://dx.doi.org/10.1109/EMBC53108.2024.10782962 | DOI Listing |
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