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10.3390/s20216321 http://scihub22266oqcxt.onion/10.3390/s20216321 33167561!7664219!33167561     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sensors+(Basel) 2020 ; 20 (21): ä Nephropedia Template TP {{tp|p=33167561|t=2020. Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review |pdf=|usr=}}{{33167561}} gab.com Text Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33167561 #FOAvip The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. However, this may be violated in EEG processing. Variations across sessions/subjects result in a deviation of the feature distribution of EEG signals in the same task, which reduces the accuracy of the decoding model for mental tasks. Recently, transfer learning (TL) has shown great potential in processing EEG signals across sessions/subjects. In this work, we reviewed 80 related published studies from 2010 to 2020 about TL application for EEG decoding. Herein, we report what kind of TL methods have been used (e.g., instance knowledge, feature representation knowledge, and model parameter knowledge), describe which types of EEG paradigms have been analyzed, and summarize the datasets that have been used to evaluate performance. Moreover, we discuss the state-of-the-art and future development of TL for EEG decoding. The results show that TL can significantly improve the performance of decoding models across subjects/sessions and can reduce the calibration time of brain-computer interface (BCI) systems. This review summarizes the current practical suggestions and performance outcomes in the hope that it will provide guidance and help for EEG research in the future. Twit Text FOAVip Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33167561 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33167561 #FOAvip English Wikipedia <ref>{{Cite pmid|33167561}}</ref> Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review #MMPMID33167561 Zhang K; Xu G; Zheng X; Li H; Zhang S; Yu Y; Liang R Sensors (Basel) 2020[Nov]; 20 (21): ä PMID33167561show ga The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. However, this may be violated in EEG processing. Variations across sessions/subjects result in a deviation of the feature distribution of EEG signals in the same task, which reduces the accuracy of the decoding model for mental tasks. Recently, transfer learning (TL) has shown great potential in processing EEG signals across sessions/subjects. In this work, we reviewed 80 related published studies from 2010 to 2020 about TL application for EEG decoding. Herein, we report what kind of TL methods have been used (e.g., instance knowledge, feature representation knowledge, and model parameter knowledge), describe which types of EEG paradigms have been analyzed, and summarize the datasets that have been used to evaluate performance. Moreover, we discuss the state-of-the-art and future development of TL for EEG decoding. The results show that TL can significantly improve the performance of decoding models across subjects/sessions and can reduce the calibration time of brain-computer interface (BCI) systems. This review summarizes the current practical suggestions and performance outcomes in the hope that it will provide guidance and help for EEG research in the future. |*Brain-Computer Interfaces[MESH] |*Electroencephalography[MESH] |*Machine Learning[MESH] |Algorithms[MESH] |Brain[MESH] |Calibration[MESH]Application of Transfer Learning in EEG Decoding Based on Brain-Comput(epmc).pdf DeepDyve Pubget Overpricing