Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network

In recent years, deep learning has emerged as a powerful tool for developing Brain–Computer Interface (BCI) systems. However, for deep learning models trained entirely on the data from a specific individual, the performance increase has only been marginal owing to the limited availability of subject...

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Bibliographic Details
Published in:	Neural networks Vol. 136; pp. 1 - 10
Main Authors:	Zhang, Kaishuo, Robinson, Neethu, Lee, Seong-Whan, Guan, Cuntai
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.04.2021
Subjects:	Adult Algorithms Brain - physiology Brain-Computer Interfaces - classification Brain–computer interface (BCI) Convolutional Neural Network (CNN) Electroencephalography (EEG) Electroencephalography - classification Electroencephalography - methods Female Hand - physiology Humans Imagination - physiology Machine Learning - classification Male Neural Networks, Computer Psychomotor Performance - physiology Transfer learning Transfer, Psychology - physiology Young Adult Electroencephalography (EEG) Transfer learning Brain–computer interface (BCI) Convolutional Neural Network (CNN)
ISSN:	0893-6080, 1879-2782, 1879-2782
Online Access:	Get full text
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