Fetching the paper…
Reading the bibliography…
The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets.
Prior knowledge in support vector kernels
Bernhard Schölkopf, Patrice Simard, Alex J Smola, and Vladimir Vapnik · 1998
Earlier work this paper cites.
Bci2000: a general-purpose brain-computer interface (bci) system
Gerwin Schalk, Dennis J McFarland, Thilo Hinterberger, Niels Birbaumer, and Jonathan R Wolpaw · 2004
Earlier work this paper cites.
Riemannian geometry
Peter Petersen, S Axler, and KA Ribet · 2006
Earlier work this paper cites.
A review of classification algorithms for eeg-based brain–computer interfaces
Fabien Lotte, Marco Congedo, Anatole Lécuyer, Fabrice Lamarche, and Bruno Arnaldi · 2007
Earlier work this paper cites.
A kernel method for the two-sample-problem
Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Schölkopf, and Alex J Smola · 2007
Earlier work this paper cites.
Filter bank common spatial pattern (fbcsp) in brain-computer interface
Kai Keng Ang, Zheng Yang Chin, Haihong Zhang, and Cuntai Guan · 2008
Earlier work this paper cites.
Multi-class filter bank common spatial pattern for four-class motor imagery bci
Zheng Yang Chin, Kai Keng Ang, Chuanchu Wang, Cuntai Guan, and Haihong Zhang · 2009
Earlier work this paper cites.
Heterogeneous domain adaptation using manifold alignment
Chang Wang and Sridhar Mahadevan · 2011
Earlier work this paper cites.
Multiclass brain–computer interface classification by riemannian geometry
Alexandre Barachant, Stéphane Bonnet, Marco Congedo, and Christian Jutten · 2011
Earlier work this paper cites.
A subject-independent brain-computer interface based on smoothed, second-order baselining
Boris Reuderink, Jason Farquhar, Mannes Poel, and Anton Nijholt · 2011
Earlier work this paper cites.
A new generation of brain-computer interface based on riemannian geometry
Marco Congedo, Alexandre Barachant, and Anton Andreev · 2013
Earlier work this paper cites.
Classification of covariance matrices using a riemannian-based kernel for bci applications
Alexandre Barachant, Stéphane Bonnet, Marco Congedo, and Christian Jutten · 2013
Cited alongside, same era.
Kernel methods on the riemannian manifold of symmetric positive definite matrices
Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann, Hongdong Li, and Mehrtash Harandi · 2013
Cited alongside, same era.
Learning euclidean-to-riemannian metric for point-to-set classification
Zhiwu Huang, Ruiping Wang, Shiguang Shan, and Xilin Chen · 2014
Cited alongside, same era.
Averaging covariance matrices for eeg signal classification based on the csp: An empirical study
Florian Yger, Fabien Lotte, and Masashi Sugiyama · 2015
Cited alongside, same era.
Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46
General Data Protection Regulation · 2016
Deep learning on lie groups for skeleton-based action recognition
Zhiwu Huang, Chengde Wan, Thomas Probst, and Luc Van Gool · 2017
Later among the works it cites.
Deep learning with convolutional neural networks for decoding and visualization of eeg pathology
R Schirrmeister, Lukas Gemein, Katharina Eggensperger, Frank Hutter, and Tonio Ball · 2017
Later among the works it cites.
Building deep networks on grassmann manifolds
Zhiwu Huang, Jiqing Wu, and Luc Van Gool · 2018
Later among the works it cites.
Learning temporal information for brain-computer interface using convolutional neural networks
Siavash Sakhavi, Cuntai Guan, and Shuicheng Yan · 2018
Later among the works it cites.
Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces
Vernon J Lawhern, Amelia J Solon, Nicholas R Waytowich, Stephen M Gordon, Chou P Hung, and Brent J Lance · 2018
Later among the works it cites.
Federated learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
Cited alongside, same era.
Riemannian approaches in brain-computer interfaces: a review
Florian Yger, Maxime Berar, and Fabien Lotte · 2016
Cited alongside, same era.
Federated learning of deep networks using model averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
Cited alongside, same era.
A riemannian network for spd matrix learning
Zhiwu Huang and Luc Van Gool · 2017
Cited alongside, same era.
Deep manifold learning of symmetric positive definite matrices with application to face recognition
Zhen Dong, Su Jia, Chi Zhang, Mingtao Pei, and Yuwei Wu · 2017
Cited alongside, same era.
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu · 2019
Later among the works it cites.
Privacy-preserving heterogeneous federated transfer learning
Dashan Gao, Yang Liu, Anbu Huang, Ce Ju, Han Yu, and Qiang Yang · 2019
Later among the works it cites.
Subject-independent brain-computer interfaces based on deep convolutional neural networks
O-Yeon Kwon, Min-Ho Lee, Cuntai Guan, and Seong-Whan Lee · 2019
Later among the works it cites.
Privacy-preserving technology to help millions of people: Federated prediction model for stroke prevention
Ce Ju, Ruihui Zhao, Jichao Sun, Xiguang Wei, Bo Zhao, Yang Liu, Hongshan Li, Tianjian Chen, Xinwei Zhang, Dashan Gao, et al · 2020
Closest in time.
Geometric foundations of data reduction
Ce Ju · 2020
Closest in time.