Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Fenqi Rong"'
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3399-3409 (2024)
The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-computer interface (MI-BCI), offering distinct advantages in applications like stroke rehabilitation. Current research predominantly concentrates on the bila
Externí odkaz:
https://doaj.org/article/088bf73aea4b40f39bd907c6149bde66
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 401-411 (2024)
Motor imagery (MI) decoding methods are pivotal in advancing rehabilitation and motor control research. Effective extraction of spectral-spatial-temporal features is crucial for MI decoding from limited and low signal-to-noise ratio electroencephalog
Externí odkaz:
https://doaj.org/article/0efe84ab780f453b81a2065ffe73c325
Autor:
Fangzhou Xu, Yunjing Miao, Yanan Sun, Dongju Guo, Jiali Xu, Yuandong Wang, Jincheng Li, Han Li, Gege Dong, Fenqi Rong, Jiancai Leng, Yang Zhang
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Abstract Deep learning networks have been successfully applied to transfer functions so that the models can be adapted from the source domain to different target domains. This study uses multiple convolutional neural networks to decode the electroenc
Externí odkaz:
https://doaj.org/article/91657dde3ad84e49b736e205c3ce48b4
Publikováno v:
2022 7th International Conference on Biomedical Signal and Image Processing (ICBIP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::229c832a57fb3fb8f14d7ddaa1fa38e4
https://doi.org/10.1145/3563737.3563755
https://doi.org/10.1145/3563737.3563755
Autor:
Yang Zhang, Jiancai Leng, Fenqi Rong, Fangzhou Xu, Tao Sun, Siddharth Siddharth, Tzyy-Ping Jung
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering. 29:2417-2424
Acquiring Electroencephalography (EEG) data is often time-consuming, laborious, and costly, posing practical challenges to train powerful but data-demanding deep learning models. This study proposes a surrogate EEG data-generation system based on cyc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::357b0037fca574467108965c18c44edf
https://doi.org/10.1109/tnsre.2021.3123969
https://doi.org/10.1109/tnsre.2021.3123969
Autor:
Yang Zhang, Gege Dong, Jiali Xu, Fenqi Rong, Miao Yunjing, Yuandong Wang, Yanan Sun, Jiancai Leng, Fangzhou Xu, Han Li, Jincheng Li, Dongju Guo
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Scientific Reports
Scientific Reports
Deep learning networks have been successfully applied to transfer functions so that the models can be adapted from the source domain to different target domains. This study uses multiple convolutional neural networks to decode the electroencephalogra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::293abf9ec28ad89d4504a5077a5a6b50
https://doi.org/10.1038/s41598-021-99114-1
https://doi.org/10.1038/s41598-021-99114-1
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9789811584619
Motor imagery-based Brain-Computer Interface (MI-BCI) has already become one of the hottest research fields and has made great achievement in stroke rehabilitation. Because it is difficult for stroke patients to complete a series of motor imagery tas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::588a0c5462472e5e78fff0667521fd2f
https://doi.org/10.1007/978-981-15-8462-6_7
https://doi.org/10.1007/978-981-15-8462-6_7
