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pro vyhledávání: '"Chungling Tu"'
Publikováno v:
Applied Sciences, Vol 14, Iss 6, p 2326 (2024)
Transfer learning (TL) utilizes knowledge from the source domain (SD) to enhance the classification rate in the target domain (TD). It has been widely used to address the challenge of sessional and inter-subject variations in electroencephalogram (EE
Externí odkaz:
https://doaj.org/article/c0bbda86d3a54d62adf20f5316ef43a9
Publikováno v:
Applied Sciences, Vol 13, Iss 8, p 5205 (2023)
Transfer learning (TL) has been proven to be one of the most significant techniques for cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCI). Hence, it is widely used to address the challenges of cross-sess
Externí odkaz:
https://doaj.org/article/6765bc3233e34042a183b34b49e77aea
Publikováno v:
Indonesian Journal of Electrical Engineering and Computer Science. 20:167
Motor imagery (MI) responses extracted from the brain in the form of EEG signals have been widely utilized for intention detection in brain computer interface (BCI) systems. However, due to the non-linearity and the non-stationarity of EEG signals, B