Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification

5 February 2022

Papers citing "Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification"

11 / 11 papers shown

Title
NiSNN-A: Non-iterative Spiking Neural Networks with Attention with Application to Motor Imagery EEG Classification Chuhan Zhang Wei Pan Cosimo Della Santina 63 0 0 28 Jan 2025
Deep Optimal Transport for Domain Adaptation on SPD Manifolds Ce Ju Cuntai Guan 82 3 0 15 Jan 2022
On the interpretation of linear Riemannian tangent space model parameters in M/EEG Reinmar J. Kobler J. Hirayama Lea Hehenberger G. Müller-Putz M. Kawanabe 29 11 0 30 Jul 2021
Federated Transfer Learning for EEG Signal Classification Ce Ju Dashan Gao R. Mane Ben Tan Yang Liu Cuntai Guan FedML 35 107 0 26 Apr 2020
Manifold-regression to predict from MEG/EEG brain signals without source modeling D. Sabbagh Pierre Ablin Gaël Varoquaux Alexandre Gramfort Denis A. Engemann 43 58 0 04 Jun 2019
Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance Matrices Mengyu Dai Zhengwu Zhang Anuj Srivastava 43 31 0 10 Apr 2019
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 136 3,848 0 10 Apr 2017
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 590 3,264 0 24 Nov 2016
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces Vernon J. Lawhern Amelia J. Solon Nicholas R. Waytowich Stephen M. Gordon C. Hung Brent Lance OOD 84 2,855 0 23 Nov 2016
A New Generation of Brain-Computer Interface Based on Riemannian Geometry M. Congedo A. Barachant Anton Andreev 59 94 0 30 Oct 2013
Invariant Scattering Convolution Networks Joan Bruna S. Mallat 102 1,272 0 05 Mar 2012