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Electroencephalography (EEG) signals are frequently used for various Brain-Computer Interface (BCI) tasks.
Ensembling neural networks: Many could be better than all
Zhi-Hua Zhou, Jianxin Wu, and Wei Tang · 2002
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A kernel method for the two-sample-problem
Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Schölkopf, and Alex Smola · 2006
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Review of the BCI Competition IV
Michael Tangermann, Klaus-Robert Müller, Ad Aertsen, Niels Birbaumer, Christoph Braun, Clemens Brunner, Robert Leeb, Carsten Mehring, Kai Miller, Gernot Mueller-Putz, Guido Nolte, Gert Pfurtscheller, Hubert Preissl, Gerwin Schalk, Alois Schlögl, Carmen Vidaurre, Stephan Waldert, and Benjamin Blankertz · 2012
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces
Fabien Lotte · 2015
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Riemannian geometry for eeg-based brain-computer interfaces; a primer and a review
Marco Congedo, Alexandre Barachant, and Rajendra Bhatia · 2017
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A riemannian network for spd matrix learning
Zhiwu Huang and Luc Van Gool · 2017
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Deep learning with convolutional neural networks for EEG decoding and visualization
Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, and Tonio Ball · 2017
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Riemannian Approaches in Brain-Computer Interfaces: A Review
Florian Yger, Maxime Berar, and Fabien Lotte · 2017
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An end-to-end deep learning approach to MI-EEG signal classification for BCIs
Hauke Dose, Jakob S. Møller, Helle K. Iversen, and Sadasivan Puthusserypady · 2018
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Transfer learning for SSVEP-based BCI using Riemannian similarities between users
Emmanuel K Kalunga, Sylvain Chevallier, and Quentin Barthélemy · 2018
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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
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A review of classification algorithms for EEG-based brain-computer interfaces: A 10-year update
Fabien Lotte, Laurent Bougrain, Andrzej Cichocki, Maureen Clerc, Marco Congedo, Alain Rakotomamonjy, and Florian Yger · 2018
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Brain–computer interfaces handbook: technological and theoretical advances
Chang S Nam, Anton Nijholt, and Fabien Lotte · 2018
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Riemannian Procrustes Analysis: Transfer learning for brain-computer interfaces
Pedro Rodrigues, Christian Jutten, and Marco Congedo · 2018
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Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces
Paolo Zanini, Marco Congedo, Christian Jutten, Salem Said, and Yannick Berthoumieu · 2018
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Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization
Amr Farahat, Christoph Reichert, Catherine M. Sweeney-Reed, and Hermann & Hinrichs · 2019
Cited alongside, same era.
Deep learning-based electroencephalography analysis: a systematic review
Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort, Tiago H Falk, and Jocelyn Faubert · 2019
Cited alongside, same era.
Transfer learning for brain-computer interfaces: A Euclidean space data alignment approach
He He and Dongrui Wu · 2020
Cited alongside, same era.
Encoding and Decoding Framework to Uncover the Algorithms of Cognition
Jean-Rémi King, Laura Gwilliams, Chris Holdgraf, Jona Sassenhagen, Alexandre Barachant, Denis Engemann, Eric Larson, and Alexandre Gramfort · 2020
Cited alongside, same era.
Thinker invariance: enabling deep neural networks for BCI across more people
Demetres Kostas and Frank Rudzicz · 2020
Cited alongside, same era.
A Bayesian-optimized design for an interpretable convolutional neural network to decode and analyze the P300 response in autism
Davide Borra, Elisa Magosso, Miguel Castelo-Branco, and Marco Simões · 2022
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Motor imagery EEG classification based on transfer learning and multi-scale convolution network
Zhanyuan Chang, Congcong Zhang, and Chuanjiang Li · 2022
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A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals
Zahra Khademi, Farideh Ebrahimi, and Hussain Montazery Kordy · 2022
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2021 BEETL Competition: Advancing Transfer Learning for Subject Independence Heterogenous EEG Data Sets
Xiaoxi Wei, A Aldo Faisal, Moritz Grosse-Wentrup, Alexandre Gramfort, Sylvain Chevallier, Vinay Jayaram, Camille Jeunet, Stylianos Bakas, Siegfried Ludwig, Konstantinos Barmpas, et al · 2022
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Transfer learning for motor imagery based brain–computer interfaces: A tutorial
Dongrui Wu, Xue Jiang, and Ruimin Peng · 2022
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Dimensionality transcending: A method for merging BCI datasets with different dimensionalities
Pedro Rodrigues, Marco Congedo, and Christian Jutten · 2020
Cited alongside, same era.
A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
Cited alongside, same era.
Deep learning-based EEG analysis: investigating P3 ERP components
Davide Borra and Elisa Magosso · 2021
Cited alongside, same era.
Inter-subject transfer learning using Euclidean alignment and transfer component analysis for motor imagery-based BCI
Orvin Demsy, David Achanccaray, and Mitsuhiro Hayashibe · 2021
Cited alongside, same era.
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning
Salim Khazem, Sylvain Chevallier, Quentin Barthélemy, Karim Haroun, and Camille Noûs · 2021
Cited alongside, same era.
Transfer learning based on hybrid Riemannian and Euclidean space data alignment and subject selection in brain-computer interfaces
Yan Li, Qingguo Wei, Yuebin Chen, and Xichen Zhou · 2021
Cited alongside, same era.
A review on transfer learning in EEG signal analysis
Zitong Wan, Rui Yang, Mengjie Huang, Nianyin Zeng, and Xiaohui Liu · 2021
Cited alongside, same era.
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Decoding movement kinematics from EEG using an interpretable convolutional neural network
Davide Borra, Valeria Mondini, Elisa Magosso, and Gernot R. Müller-Putz · 2023
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Pseudo-online framework for BCI evaluation: a MOABB perspective using various MI and SSVEP datasets
Igor Carrara and Theodore Papadopoulo · 2023
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Pierre Guetschel and Michael Tangermann · 2023
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LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interfaces and interpretability
Zhengqing Miao, Meirong Zhao, Xin Zhang, and Dong Ming · 2023
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Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
Yassine El Ouahidi, Lucas Drumetz, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, and Vincent Gripon · 2023
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k-Fold Cross-Validation Can Significantly Over-Estimate True Classification Accuracy in Common EEG-Based Passive BCI Experimental Designs: An Empirical Investigation
Jacob White and Sarah D Power · 2023
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Deep riemannian networks for eeg decoding, 2023
Daniel Wilson, Robin Tibor Schirrmeister, Lukas Alexander Wilhelm Gemein, and Tonio Ball · 2023
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An evaluation of transfer learning models in EEG-based authentication
Hui Yen Yap, Yun-Huoy Choo, Zeratul Izzah Mohd Yusoh, and Wee How Khoh · 2023
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Geometric Neural Network based on Phase Space for BCI decoding
Igor Carrara, Bruno Aristimunha, Marie-Constance Corsi, Raphael Y. de Camargo, Sylvain Chevallier, and Théodore Papadopoulo · 2024
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Apolline Mellot, Antoine Collas, Sylvain Chevallier, Denis Alexander Engemann, and Alexandre Gramfort · 2024
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Bnmtrans: A brain network sequence-driven manifold-based transformer for cognitive impairment detection using eeg
Ruihan Qin, Zhenxi Song, Huixia Ren, Zian Pei, Lin Zhu, Xue Shi, Yi Guo, Honghai Liu, Min Zhang, and Zhiguo Zhang · 2024
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Calibration-free online test-time adaptation for electroencephalography motor imagery decoding
Martin Wimpff, Mario Döbler, and Bin Yang · 2024
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