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Objective: The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision.
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Detecting strange attractors in turbulence
Floris Takens · 1981
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The takens embedding theorem
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Florias Takens · 1993
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Mental imagery in the motor context
Marc Jeannerod · 1995
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Primary motor and sensory cortex activation during motor performance and motor imagery: a functional magnetic resonance imaging study
Carlo A Porro, Maria Pia Francescato, Valentina Cettolo, Mathew E Diamond, Patrizia Baraldi, Chiara Zuiani, Massimo Bazzocchi, and Pietro E Di Prampero · 1996
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Motor imagery activates primary sensorimotor area in humans
Gert Pfurtscheller and Christa Neuper · 1997
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Diffusion tensor imaging: concepts and applications
Denis Le Bihan, Jean-François Mangin, Cyril Poupon, Chris A Clark, Sabina Pappata, Nicolas Molko, and Hughes Chabriat · 2001
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A metric for covariance matrices
Wolfgang Förstner and Boudewijn Moonen · 2003
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Identifying true brain interaction from EEG data using the imaginary part of coherency
Guido Nolte, Ou Bai, Lewis Wheaton, Zoltan Mari, Sherry Vorbach, and Mark Hallett · 2004
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Visualization and processing of tensor fields
Joachim Weickert and Hans Hagen · 2005
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A differential geometric approach to the geometric mean of symmetric positive-definite matrices
Maher Moakher · 2005
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Erd/ers patterns reflecting sensorimotor activation and deactivation
Christa Neuper, Michael Wörtz, and Gert Pfurtscheller · 2006
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Roberto D Pascual-Marqui · 2007
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Geometric means in a novel vector space structure on symmetric positive-definite matrices
Vincent Arsigny, Pierre Fillard, Xavier Pennec, and Nicholas Ayache · 2007
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Brain–computer communication: motivation, aim, and impact of exploring a virtual apartment
Robert Leeb, Felix Lee, Claudia Keinrath, Reinhold Scherer, Horst Bischof, and Gert Pfurtscheller · 2007
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Sensorimotor EEG patterns during motor imagery in hemiparetic stroke patients
Reinhold Scherer, Andrea Mohapp, Peter Grieshofer, Gert Pfurtscheller, and Christa Neuper · 2007
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Brain–computer interfaces in neurological rehabilitation
Janis J Daly and Jonathan R Wolpaw · 2008
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Cognitive motor processes: The role of motor imagery in the study of motor representations
Jörn Munzert, Britta Lorey, and Karen Zentgraf · 2008
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On over-fitting in model selection and subsequent selection bias in performance evaluation
Gavin C Cawley and Nicola LC Talbot · 2010
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Riemannian geometry applied to bci classification
Alexandre Barachant, Stéphane Bonnet, Marco Congedo, and Christian Jutten · 2010
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Learning from other subjects helps reducing brain-computer interface calibration time
Fabien Lotte and Cuntai Guan · 2010
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Multiclass brain–computer interface classification by Riemannian geometry
Alexandre Barachant, Stéphane Bonnet, Marco Congedo, and Christian Jutten · 2011
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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 J Miller, Gernot Mueller-Putz, et al · 2012
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MEG and EEG data analysis with MNE-Python
Alexandre Gramfort, Martin Luessi, Eric Larson, Denis Engemann, Daniel Strohmeier, Christian Brodbeck, Roman Goj, Mainak Jas, Teon Brooks, Lauri Parkkonen, and Matti Hämäläinen · 2013
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Optimal state-space reconstruction using derivatives on projected manifold
Chetan Nichkawde · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery
Weibo Yi, Shuang Qiu, Kun Wang, Hongzhi Qi, Lixin Zhang, Peng Zhou, Feng He, and Dong Ming · 2014
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Autoreject: Automated artifact rejection for MEG and EEG data
Mainak Jas, Denis Alexander Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre Gramfort · 2016
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A fully automated trial selection method for optimization of motor imagery based brain-computer interface
Bangyan Zhou, Xiaopei Wu, Zhao Lv, Lei Zhang, and Xiaojin Guo · 2016
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Riemannian approaches in brain-computer interfaces: a review
Florian Yger, Maxime Berar, and Fabien Lotte · 2016
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EEG datasets for motor imagery brain–computer interface
Hohyun Cho, Minkyu Ahn, Sangtae Ahn, Moonyoung Kwon, and Sung Chan Jun · 2017
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MOABB: trustworthy algorithm benchmarking for BCIs
Vinay Jayaram and Alexandre Barachant · 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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Faster ICA under orthogonal constraint
Pierre Ablin, Jean-François Cardoso, and Alexandre Gramfort · 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
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
Reinmar Kobler, Jun-ichiro Hirayama, Qibin Zhao, and Motoaki Kawanabe · 2022
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DreamNet: A Deep Riemannian Manifold Network for SPD Matrix Learning
Rui Wang, Xiao-Jun Wu, Ziheng Chen, Tianyang Xu, and Josef Kittler · 2022
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Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification
Ce Ju and Cuntai Guan · 2022
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Deep Riemannian Networks for EEG Decoding
Daniel Wilson, Robin Tibor Schirrmeister, Lukas Alexander Wilhelm Gemein, and Tonio Ball · 2022
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Robust learning from corrupted eeg with dynamic spatial filtering
Hubert Banville, Sean UN Wood, Chris Aimone, Denis-Alexander Engemann, and Alexandre Gramfort · 2022
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Riemannian adaptive optimization methods
Gary Bécigneul and Octavian-Eugen Ganea · 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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Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian · 2018
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Deep learning-based electroencephalography analysis: a systematic review
Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort, Tiago H Falk, and Jocelyn Faubert · 2019
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Wearable EEG and beyond
Alexander J Casson · 2019
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Riemannian batch normalization for SPD neural networks
Daniel Brooks, Olivier Schwander, Frederic Barbaresco, Jean-Yves Schneider, and Matthieu Cord · 2019
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Mother of all bci benchmarks v1.0
Bruno Aristimunha, Igor Carrara, Pierre Guetschel, Sara Sedlar, Pedro Rodrigues, Jan Sosulski, Divyesh Narayanan, Erik Bjareholt, Barthelemy Quentin, Robin Tibor Schirrmeister, Emmanuel Kalunga, Ludovic Darmet, Cattan Gregoire, Ali Abdul Hussain, Ramiro Gatti, Vladislav Goncharenko, Jordy Thielen, Thomas Moreau, Yannick Roy, Vinay Jayaram, Alexandre Barachant, and Sylvain Chevallier · 2023
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Machine learning of brain-specific biomarkers from EEG
Philipp Bomatter, Joseph Paillard, Pilar Garces, Jörg Hipp, and Denis Engemann · 2023
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A Strong and Simple Deep Learning Baseline for BCI MI Decoding
Yassine El Ouahidi, Vincent Gripon, Bastien Pasdeloup, Ghaith Bouallegue, Nicolas Farrugia, and Giulia Lioi · 2023
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A discriminative SPD feature learning approach on Riemannian manifolds for EEG classification
Byung Hyung Kim, Jin Woo Choi, Honggu Lee, and Sungho Jo · 2023
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Jianchao Lu, Yuzhe Tian, Yang Zhang, Jiaqi Ge, Quan Z. Sheng, and Xianglin Zheng · 2023
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Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective From the Time–Frequency Analysis
Ce Ju and Cuntai Guan · 2023
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U-SPDNet: An SPD manifold learning-based neural network for visual classification
Rui Wang, Xiao-Jun Wu, Tianyang Xu, Cong Hu, and Josef Kittler · 2023
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Reducing the Dimensionality of SPD Matrices with Neural Networks in BCI
Zhen Peng, Hongyi Li, Di Zhao, and Chengwei Pan · 2023
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Functional connectivity learning via Siamese-based SPD matrix representation of brain imaging data
Yunbo Tang, Dan Chen, Jia Wu, Weiping Tu, Jessica J.M. Monaghan, Paul Sowman, and David Mcalpine · 2023
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Diffusion Models for Constrained Domains
Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, and Michael Hutchinson · 2023
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Multivariate phase space reconstruction and Riemannian manifold for sleep stage classification
Xueling Zhou, Bingo Wing-Kuen Ling, Waqar Ahmed, Yang Zhou, Yuxin Lin, and Hongtao Zhang · 2023
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Unifying pairwise interactions in complex dynamics
Oliver M Cliff, Annie G Bryant, Joseph T Lizier, Naotsugu Tsuchiya, and Ben D Fulcher · 2023
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Reproducibility analysis of functional connectivity measures for application in motor imagery BCIs
Pedro Felipe Giarusso de Vazquez, Carlos Alberto Stefano Filho, Gabriel Chaves de Melo, Arturo Forner-Cordero, and Gabriela Castellano · 2023
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Connectivity analysis in EEG data: a tutorial review of the state of the art and emerging trends
Giovanni Chiarion, Laura Sparacino, Yuri Antonacci, Luca Faes, and Luca Mesin · 2023
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Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology
Eugene Tan, Shannon Algar, Débora Corrêa, Michael Small, Thomas Stemler, and David Walker · 2023
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Shuhei Watanabe · 2023
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pyRiemann/pyRiemann: v0.5
Alexandre Barachant, Quentin Barthélemy, Jean-Rémi King, Alexandre Gramfort, Sylvain Chevallier, Pedro L. C. Rodrigues, Emanuele Olivetti, Vladislav Goncharenko, Gabriel Wagner vom Berg, Ghiles Reguig, Arthur Lebeurrier, Erik Bjäreholt, Maria Sayu Yamamoto, Pierre Clisson, and et al. Marie-Constance Corsi · 2023
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An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth
Akshay Sujatha Ravindran and Jose Contreras-Vidal · 2023
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A generic noninvasive neuromotor interface for human-computer interaction
Ctrl labs at Reality Labs, David Sussillo, Patrick Kaifosh, and Thomas Reardon · 2024
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Classification of bci-eeg based on the augmented covariance matrix
Igor Carrara and Théodore Papadopoulo · 2024
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Brain decoding: toward real-time reconstruction of visual perception
Yohann Benchetrit, Hubert Banville, and Jean-Rémi King · 2024
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Eeg motor imagery decoding: A framework for comparative analysis with channel attention mechanisms
Martin Wimpff, Leonardo Gizzi, Jan Zerfowski, and Bin Yang · 2024
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Unsupervised adaptive deep learning method for bci motor imagery decoding
Yassine El Ouahidi, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, and Vincent Gripon · 2024
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The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
Sylvain Chevallier, Igor Carrara, Bruno Aristimunha, Pierre Guetschel, Sara Sedlar, Bruna Lopes, Sebastien Velut, Salim Khazem, and Thomas Moreau · 2024
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