Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Ming-ai Li"'
Publikováno v:
IEEE Access, Vol 9, Pp 86994-87006 (2021)
Deep neural network is a promising method to recognize motor imagery electroencephalography (MI-EEG), which is often used as the source signal of a rehabilitation system; and the core issues are the data representation and the matched neural networks
Externí odkaz:
https://doaj.org/article/2d30a4f246864491a35164c8192d90c2
Publikováno v:
IEEE Access, Vol 8, Pp 3197-3211 (2020)
Combination of the Motor Imagery EEG (MI-EEG) imaging and Deep Convolutional Neural Network is a prospective recognition method in brain computer interface. Nowadays, the frequency or timefrequency analysis has been applied to each channel of MI-EEG
Externí odkaz:
https://doaj.org/article/8a788c4ee0ee41708b7267d9e0de307e
Publikováno v:
Applied Sciences, Vol 7, Iss 1, p 92 (2017)
Electroencephalography (EEG) is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG) not only has a close correlation with the human imagination and movement intention
Externí odkaz:
https://doaj.org/article/2b523e3160a54a8fac2d217f51c49f4b
Autor:
Ming-ai Li, Zi-wei Ruan
Publikováno v:
Cognitive Neurodynamics. 17:445-457
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2566c91c0b1c5299edfb6831f1a0591c
https://doi.org/10.1007/s11571-022-09826-x
https://doi.org/10.1007/s11571-022-09826-x
Publikováno v:
Machine Intelligence Research. 19:247-256
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a44268151a281d3191901fbd21966c7b
https://doi.org/10.1007/s11633-022-1327-2
https://doi.org/10.1007/s11633-022-1327-2
Publikováno v:
Technology and Health Care. 29:921-937
BACKGROUND: Motor imagery electroencephalogram (MI-EEG) play an important role in the field of neurorehabilitation, and a fuzzy support vector machine (FSVM) is one of the most used classifiers. Specifically, a fuzzy c-means (FCM) algorithm was used
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90bc91b78c3ba2485d0ede6a43b0d028
https://doi.org/10.3233/thc-202619
https://doi.org/10.3233/thc-202619
A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification
Autor:
Dong-qin Xu, Ming-ai Li
Publikováno v:
Applied intelligence (Dordrecht, Netherlands).
Domain adaptation, as an important branch of transfer learning, can be applied to cope with data insufficiency and high subject variabilities in motor imagery electroencephalogram (MI-EEG) based brain-computer interfaces. The existing methods general
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ea9cd37b81a073d340dd831592f9bcb
https://pubmed.ncbi.nlm.nih.gov/36039116
https://pubmed.ncbi.nlm.nih.gov/36039116
Publikováno v:
IEEE Access, Vol 9, Pp 67405-67416 (2021)
The breakthrough of electroencephalogram (EEG) signal classification of brain computer interface (BCI) will set off another technological revolution of human computer interaction technology. Because the collected EEG is a type of nonstationary signal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23172165addfb2ba68299c5dd00ac67b
https://doi.org/10.1109/access.2021.3076347
https://doi.org/10.1109/access.2021.3076347
Publikováno v:
IEEE Access, Vol 8, Pp 3197-3211 (2020)
Combination of the Motor Imagery EEG (MI-EEG) imaging and Deep Convolutional Neural Network is a prospective recognition method in brain computer interface. Nowadays, the frequency or timefrequency analysis has been applied to each channel of MI-EEG
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49371a04214e9c488216164cd4734fc2
https://ieeexplore.ieee.org/document/8944067/
https://ieeexplore.ieee.org/document/8944067/
Autor:
Ming-Ai Li, Dong-Qin Xu
Publikováno v:
2021 33rd Chinese Control and Decision Conference (CCDC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c32c93d03a0a42b3f100f7de6b969bd0
https://doi.org/10.1109/ccdc52312.2021.9602818
https://doi.org/10.1109/ccdc52312.2021.9602818