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Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.
Zhang K, Xu G, Zheng X, Li H, Zhang S, Yu Y, Liang R. Zhang K, et al. Sensors (Basel). 2020 Nov 5;20(21):6321. doi: 10.3390/s20216321. Sensors (Basel). 2020. PMID: 33167561 Free PMC article. Review.
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 distrib …
The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main ass …
Data Augmentation for Motor Imagery Signal Classification Based on a Hybrid Neural Network.
Zhang K, Xu G, Han Z, Ma K, Zheng X, Chen L, Duan N, Zhang S. Zhang K, et al. Sensors (Basel). 2020 Aug 11;20(16):4485. doi: 10.3390/s20164485. Sensors (Basel). 2020. PMID: 32796607 Free PMC article.
As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery (MI) has been widely used in the fields of neurological rehabilitation and robot control. Recently, researchers have proposed various methods for feature extraction and classification b …
As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery (MI) has been widely used in the fields of neurologi …