Fetching the paper…
Reading the bibliography…
State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning (DL) or Riemannian-Geometry-based decoders (RBDs).
“Riemannian batch normalization for SPD neural networks” arXiv: 1909.02414
Daniel Brooks et al · 1909
Earlier work this paper cites.
“PyTorch: An Imperative Style, High-Performance Deep Learning Library” arXiv:1912.01703 [cs, stat]
Adam Paszke et al · 1912
Earlier work this paper cites.
“Motor imagery and direct brain-computer communication” Conference Name: Proceedings of the IEEE
G. Pfurtscheller and C. Neuper · 2001
Earlier work this paper cites.
“ManifoldNorm: Extending normalizations on Riemannian Manifolds” arXiv: 2003.13869
Rudrasis Chakraborty · 2003
Earlier work this paper cites.
“Geoopt: Riemannian Optimization in PyTorch” arXiv:2005.02819 [cs]
Max Kochurov, Rasul Karimov and Serge Kozlukov · 2005
Earlier work this paper cites.
“Log-Euclidean metrics for fast and simple calculus on diffusion tensors”
Vincent Arsigny, Pierre Fillard, Xavier Pennec and Nicholas Ayache · 2006
Earlier work this paper cites.
“Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment”
Robert Leeb et al · 2007
Earlier work this paper cites.
“Matplotlib: A 2D Graphics Environment” Conference Name: Computing in Science & Engineering
John. Hunter · 2007
Earlier work this paper cites.
“RFNet: Riemannian Fusion Network for EEG-based Brain-Computer Interfaces” arXiv: 2008.08633
Guangyi Zhang and Ali Etemad · 2008
Earlier work this paper cites.
“An adaptive filter bank for motor imagery based Brain Computer Interface” ISSN: 1558-4615
Kavitha. Thomas, Cuntai Guan, Lau Tong and Vinod. Prasad · 2008
Earlier work this paper cites.
“Movement related activity in the high gamma range of the human EEG”
Tonio Ball et al · 2008
Earlier work this paper cites.
“Discriminative FilterBank selection and EEG information fusion for Brain Computer Interface” ISSN: 2158-1525
Kavitha. Thomas, Cuntai Guan, Lau Tong and A.. Vinod · 2009
Earlier work this paper cites.
“Neural Architecture Search of SPD Manifold Networks” arXiv: 2010.14535
Rhea Sukthanker et al · 2010
Earlier work this paper cites.
“Data Structures for Statistical Computing in Python”, 2010, pp. 56–61
Wes McKinney · 2010
Earlier work this paper cites.
“Scikit-learn: Machine Learning in Python”
Fabian Pedregosa et al · 2011
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 · 2012
Earlier work this paper cites.
“Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b”
Kai Ang et al · 2012
Earlier work this paper cites.
“Review of the BCI Competition IV”
Michael Tangermann et al · 2012
Earlier work this paper cites.
“MEG and EEG data analysis with MNE-Python”
Alexandre Gramfort et al · 2013
Earlier work this paper cites.
“An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery”
Kais Belwafi, Ridha Djemal, Fakhreddine Ghaffari and Olivier Romain · 2014
Earlier work this paper cites.
“Bayesian Optimization: Open source constrained global optimization tool for Python”, 2014–
Fernando Nogueira · 2014
Earlier work this paper cites.
“Deep learning”
Yann LeCun, Yoshua Bengio and Geoffrey Hinton · 2015
Earlier work this paper cites.
“Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification”, 2015, pp. 10
Zhiwu Huang et al · 2015
Earlier work this paper cites.
“Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices”
Lei Wang et al · 2015
Cited alongside, same era.
“Riemannian Approaches in Brain-Computer Interfaces: A Review”
Florian Yger, Maxime Berar and Fabien Lotte · 2016
Cited alongside, same era.
“A Riemannian Network for SPD Matrix Learning”, 2016, pp. 7
Zhiwu Huang and Luc Gool · 2016
Cited alongside, same era.
“Open Access Dataset for EEG+NIRS Single-Trial Classification”
Jaeyoung Shin et al · 2016
Cited alongside, same era.
“Deep learning with convolutional neural networks for EEG decoding and visualization”
Robin Schirrmeister et al · 2017
Cited alongside, same era.
“Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review” Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/2326263X.2017.1297192
“Temporally Adaptive Common Spatial Patterns with Deep Convolutional Neural Networks”
Mahta Mousavi and Virginia. de Sa · 2019
Later among the works it cites.
“A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding” Conference Name: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Yang Li et al · 2019
Later among the works it cites.
“Second-Order Networks in PyTorch”
Daniel Brooks et al · 2019
Later among the works it cites.
“Interpretable Convolutional Filters with SincNet” arXiv:1811.09725 [cs, eess]
Mirco Ravanelli and Yoshua Bengio · 2019
Later among the works it cites.
“EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy”
Min-Ho Lee et al · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Marco Congedo, Alexandre Barachant and Rajendra Bhatia · 2017
Cited alongside, same era.
“Deep Manifold Learning of Symmetric Positive Definite Matrices with Application to Face Recognition”, 2017, pp. 7
Zhen Dong et al · 2017
Cited alongside, same era.
“Second-order Convolutional Neural Networks” arXiv: 1703.06817
Kaicheng Yu and Mathieu Salzmann · 2017
Cited alongside, same era.
“Prediction of fatigue-related driver performance from EEG data by deep Riemannian model” ISSN: 1558-4615
Mehdi Hajinoroozi, Jianqiu Zhang and Yufei Huang · 2017
Cited alongside, same era.
“Driver’s fatigue prediction by deep covariance learning from EEG”
M. Hajinoroozi, J.. Zhang and Y. Huang · 2017
Cited alongside, same era.
“EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces”
Vernon. Lawhern et al · 2018
Cited alongside, same era.
“A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update” Publisher: IOP Publishing, 2018, pp. 031005
F. Lotte et al · 2018
Cited alongside, same era.
“The Riemannian Minimum Distance to means field Classifier”
Marco Congedo, Pedro Rodrigues and Christian Jutten · 2019
Later among the works it cites.
“Moving beyond P values: data analysis with estimation graphics” Publisher: Nature Publishing Group
Joses Ho et al · 2019
Later among the works it cites.
“The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks” arXiv:1803.03635 [cs]
Jonathan Frankle and Michael Carbin · 2019
Later among the works it cites.
“Manifold-valued image processing with SPD matrices”
Xavier Pennec · 2020
Later among the works it cites.
“MLP With Riemannian Covariance for Motor Imagery Based EEG Analysis” Conference Name: IEEE Access
P. Yang, J. Wang, H. Zhao and R. Li · 2020
Later among the works it cites.
“Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG”
Most Akter et al · 2020
Later among the works it cites.
“Second-Order Convolutional Neural Network Based on Cholesky Compression Strategy”
Yan Li, Jing Zhang and Qiang Hua · 2020
Later among the works it cites.
“SciPy 1.0: fundamental algorithms for scientific computing in Python” Number: 3 Publisher: Nature Publishing Group
Pauli Virtanen et al · 2020
Later among the works it cites.
“Array programming with NumPy”
Charles. Harris et al · 2020
Later among the works it cites.
“Riemannian Embedding Banks for Common Spatial Patterns with EEG-based SPD Neural Networks”
Yoon-Je Suh and Byung Kim · 2021
Later among the works it cites.
“A New Subject-Specific Discriminative and Multi-Scale Filter Bank Tangent Space Mapping Method for Recognition of Multiclass Motor Imagery”
Fan Wu et al · 2021
Later among the works it cites.
Ravikiran Mane et al · 2021
Later among the works it cites.
“seaborn: statistical data visualization”
Michael. Waskom · 2021
Later among the works it cites.
“Riemannian geometry for combining functional connectivity metrics and covariance in BCI”
Sylvain Chevallier, Marie-Constance Corsi, Florian Yger and Fabrizio De · 2022
Closest in time.
“Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification”
Ce Ju and Cuntai Guan · 2022
Closest in time.
Reinmar. Kobler, Jun-ichiro Hirayama, Qibin Zhao and Motoaki Kawanabe · 2022
Closest in time.
“Ear-EEG Sensitivity Modelling for Neural and Artifact Sources” arXiv:2207.08497 [physics]
Metin Yarici, Mike Thornton and Danilo Mandic · 2022
Closest in time.