Single-Source to Single-Target Cross-Subject Motor Imagery Classification Based on Multisubdomain Adaptation Network |
Yi Chen, et al. |
Jul-2022 |
IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Paper Code |
82.61%(STS) |
BCIC_III_IVa |
Domain-adaptation CNN |
Golden subject is everyone: A subject transfer neural network for motor imagery-based brain computer interfaces |
Biao Sun, et al. |
Jul-2022 |
Neural Networks |
Paper Code |
80.7% (10 CV) |
BCIC_IV_2b |
Style_transfer |
Feature Extraction Method based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI |
Hua Fang, et al. |
Jun-2022 |
IEEE Journal of Biomedical and Health Informatics |
Paper Code |
77.7% (10 CV) 86.9% (10 CV) |
BCIC_IV_2a BCIC_IV_2b |
FB Riemann SVM |
Improving the performance of multisubject motor imagery-based BCIs using twin cascaded softmax CNNs |
Jing Luo, et al. |
Mar-2021 |
Journal of Neural Engineering |
Paper Code |
78.88% 87.20% |
BCIC_IV_2a (left, right hand) BCIC_IV_2b |
Plug-and-play Twin-CNN |
A novel motor imagery EEG decoding method based on feature separation |
Lie Yang, et al. |
Mar-2021 |
Journal of Neural Engineering |
Paper Code |
83.97% (10 fold CV) 86.91% (10 fold CV) |
BCIC_IV_2a BCIC_IV_2b |
CNN |
Motor Imagery EEG Decoding Method Based on a Discriminative Feature Learning Strategy |
Lie Yang, et al. |
Jan-2021 |
IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Paper Code |
81.85% (10 fold CV) 85.46% |
BCIC_IV_2a BCIC_IV_2b |
CNN |
EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Network for Embedded Motor-Imagery Brain-Machine Interfaces |
Thorir Mar Ingolfsson et al. |
Oct-2020 |
IEEE International Conference on Systems, Man, and Cybernetics |
Paper Code |
83.84% |
BCIC_IV_2a |
CNN |
MMCNN: A multi-branch multi-scale convolutional neural network for motor imagery classification |
Jia, Ziyu, et al. |
Sep-2020 |
ECML-PKDD 2020 |
Paper Code |
81.4%(5CV) 84.4%(5CV) |
BCIC_IV_2a (left hand, right hand) BCIC_IV_2b |
CNN |
Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination |
Davide Borra, et al. |
May-2020 |
Neural Networks |
Paper Code |
91.2% 72.8% |
HGD BCIC_IV_2a |
CNN |
Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach |
He He, et al. |
Feb-2020 |
IEEE Transactions on Biomedical Engineering |
Paper Code |
79.79% (LOSO) 73.53% (LOSO) |
BCIC_IV_1 BCIC_IV_2a |
CSP LDA |
Classification of Motor Imagery Task by Using Novel Ensemble Pruning Approach |
Muhammad Ammar Ali, et al. |
Jan-2020 |
IEEE Tansactions of Fuzzy Systems |
Paper Code |
0.7784(AUC) |
BCIC_II_III |
SVM |
A Parallel Multiscale Filter Bank Convoluitonal Neural Networks for Motor Imagery EEG Classification |
Hao Wu, et al. |
Nov-2019 |
froniers in Neuroscience |
Paper Code |
75.8% 84.3% 94.4% |
BCIC_IV_2a BCIC_IV_2b HGD |
CNN |
A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification |
Xinqiao Zhao, et al. |
Oct-2019 |
IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Paper Code |
64.4(Kappa) 75.015%(10 fold CV) |
BCIC_IV_2a |
3D CNN |
Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion |
Syed Umar Amin, et al. |
Jul-2019 |
Future Generation Computer Systems |
Paper Code |
75.7% 95.4% |
BCIC_IV_2a HGD |
AE CNN |
Sparse Group Representation Model for Motor Imagery EEG Classification |
Yong Jiao, et al. |
Mar-2019 |
IEEE Journal of Biomedical and Health Informatics |
Paper Code |
78.2% 77.2% |
BCIC_IV_2b BCIC_III_IVa |
FBCSP Dictionary_leraning |
Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks |
Siavash Sakhavi, et al. |
Nov-2018 |
IEEE Transactions on Neural Networks and Learning Systems |
Paper Code |
74.46% |
BCIC_IV_2a |
CNN |
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces |
Vernon J Lawhern, et al. |
Jul-2018 |
Journal of Neural Enginnering |
Paper Code |
68±a% |
BCIC_IV_2a |
CNN |
Deep Learning With Convolutional Neural Networks for EEG Decoding and Visualization |
Robin Tibor Schirrmeister, et al. |
Aug-2017 |
Human Brain Mapping |
Paper Code |
73.7% 93.9% 89.9(Kappa) 72.2% |
BCIC_IV_2a HGD BCIC_IV_2b MID |
CNN |