Improving cross-session motor imagery decoding performance with data augmentation and domain adaptation
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(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works
A survey on Image Data Augmentation for Deep Learning
Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203 · see citing works
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927 · see citing works
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906 · see citing works
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328 · see citing works
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115 · see citing works
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Bai, S., Kolter, J. Z., & Koltun, V. (2018) · arXiv · doi:10.48550/arxiv.1803.01271 · Cited by 4,655 · see citing works
Learning Transferable Features with Deep Adaptation Networks
Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015) · arXiv · doi:10.48550/arxiv.1502.02791 · Cited by 4,505 · see citing works
Deep learning with convolutional neural networks for EEG decoding and visualization
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23730 · Cited by 3,751 · see citing works
Motor imagery and direct brain-computer communication
Pfurtscheller, G., & Neuper, C. (2001) · Proceedings of the IEEE · doi:10.1109/5.939829 · Cited by 1,990 · see citing works
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface
Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C. (2008) · 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) · doi:10.1109/ijcnn.2008.4634130 · Cited by 1,514 · see citing works
Deep Subdomain Adaptation Network for Image Classification
Zhu, Y., Zhuang, F., Wang, J., Ke, G., Chen, J., Bian, J., Xiong, H., & He, Q. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2988928 · Cited by 1,257 · see citing works
EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization
Song, Y., Zheng, Q., Liu, B., & Gao, X. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3230250 · Cited by 1,139 · see citing works
A survey of unsupervised deep domain adaptation
Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987 · see citing works
Multi-Adversarial Domain Adaptation
Pei, Z., Cao, Z., Long, M., & Wang, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11767 · Cited by 922 · see citing works
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Shen, J. G., Qu, Y., Zhang, W., & Yu, Y. H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11784 · Cited by 895 · see citing works
Multiclass Brain–Computer Interface Classification by Riemannian Geometry
Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2172210 · Cited by 878 · see citing works
A Brief Review of Domain Adaptation
Farahani, A., Voghoei, S., Rasheed, K., & Arabnia, H. R. (2021) · Transactions on computational science and computational intelligence · doi:10.1007/978-3-030-71704-9_65 · Cited by 679 · see citing works
Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach
He, H., & Wu, D. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2913914 · Cited by 525 · see citing works
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016
Wu, D., Xu, Y., & Lu, B.-L. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3007453 · Cited by 434 · see citing works
Transfer Learning: A Riemannian Geometry framework with applications to Brain-Computer Interfaces
Zanini, P., Congedo, M., Jutten, C., Said, S., & Berthoumieu, Y. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2742541 · Cited by 412 · see citing works
Data augmentation for deep-learning-based electroencephalography
Lashgari, E., Liang, D., & Maoz, U. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108885 · Cited by 398 · see citing works
A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition
Li, Y., Zheng, W., Zong, Y., Cui, Z., Zhang, T., & Zhou, X. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2885474 · Cited by 392 · see citing works
An end-to-end deep learning approach to MI-EEG signal classification for BCIs
Dose, H., Møller, J. S., Iversen, H. K., & Puthusserypady, S. K. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.08.031 · Cited by 385 · see citing works
Noninvasive neuroimaging enhances continuous neural tracking for robotic device control
Edelman, B. J., Meng, J., Suma, D., Zurn, C., Nagarajan, E., Baxter, B. S., Cline, C. C., & He, B. (2019) · Science Robotics · doi:10.1126/scirobotics.aaw6844 · Cited by 380 · see citing works
Data augmentation approaches in natural language processing: A survey
Li, B., Hou, Y., & Che, W. (2022) · AI Open · doi:10.1016/j.aiopen.2022.03.001 · Cited by 357 · see citing works
Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives
Liu, X., Yoo, C., Xing, F., Oh, H., Fakhri, G. E., Kang, J., & Woo, J. (2022) · APSIPA Transactions on Signal and Information Processing · doi:10.1561/116.00000192 · Cited by 303 · see citing works
Controlling a Wheelchair Indoors Using Thought
Rebsamen, B., Burdet, E., Guan, C., Zhang, H., Teo, C. L., Zeng, Q., Laugier, C., & Ang, M. H. (2007) · IEEE Intelligent Systems · doi:10.1109/mis.2007.26 · Cited by 274 · see citing works
Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification
Zhao, H., Zheng, Q., Ma, K., Li, H., & Zheng, Y. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3010780 · Cited by 255 · see citing works
EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN
Luo, Y., & Lu, B. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512865 · Cited by 228 · see citing works
Zhang, C., Kim, Y., & Eskandarian, A. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abed81 · Cited by 225 · see citing works
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
Schirrmeister, R. T., Gemein, L. A. W., Eggensperger, K., Hutter, F., & Tonio Ball, A. (2017) · IEEE Signal Processing in Medicine and Biology Symposium · doi:10.1109/spmb.2017.8257015 · Cited by 221 · see citing works
Weighted Transfer Learning for Improving Motor Imagery-Based Brain–Computer Interface
Azab, A. M., Mihaylova, L. S., Ang, K. K., & Arvaneh, M. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2923315 · Cited by 175 · see citing works
Dynamic Joint Domain Adaptation Network for Motor Imagery Classification
Hong, X., Zheng, Q., Liu, L., Chen, P., Ma, K., Gao, Z., & Zheng, Y. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3059166 · Cited by 134 · see citing works
She, Q., Chen, T., Fang, F. Z., Zhang, J., Gao, Y., & Zhang, Y. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3241846 · Cited by 131 · see citing works
Event-related dynamics of brain oscillations
Neuper, C., & Klimesch, W. (2006) · Cited by 114 · see citing works
Light-weight residual convolution-based capsule network for EEG emotion recognition
Fan, C., Wang, J., Wei, H., Yang, X., Pei, G., Li, T., & Lv, Z. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102522 · Cited by 100 · see citing works
Data augmentation for self-paced motor imagery classification with C-LSTM.
Freer, D., & Yang, G.-Z. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab57c0 · Cited by 96 · see citing works
Pei, Y., Luo, Z., Yan, Y., Yan, H., Jiang, J., Li, W., Xie, L., & Yin, E. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.645952 · Cited by 95 · see citing works
A Temporal Dependency Learning CNN With Attention Mechanism for MI-EEG Decoding
Ma, X., Chen, W., Pei, Z., Liu, J., Huang, B., & Chen, J. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3299355 · Cited by 94 · see citing works
Xu, M., He, F., Jung, T., Gu, X., & Ming, D. (2021) · Engineering · doi:10.1016/j.eng.2021.09.011 · Cited by 93 · see citing works
Hilbert transform for brain waves
Freeman, W. (2007) · Scholarpedia · doi:10.4249/scholarpedia.1338 · Cited by 88 · see citing works
Global Adaptive Transformer for Cross-Subject Enhanced EEG Classification
Song, Y., Zheng, Q., Wang, Q., Gao, X., & Heng, P. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3285309 · Cited by 63 · see citing works
Liu, X., Lv, L., Shen, Y., Xiong, P., Yang, J., & Liu, J. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abd82b · Cited by 55 · see citing works
Mind the Pad -- CNNs can Develop Blind SpotsPreprint
Alsallakh, B., Kokhlikyan, N., Miglani, V., Yuan, J., & Reblitz-Richardson, O. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02178 · Cited by 55 · see citing works
Classification of long sequential data using circular dilated convolutional neural networks
Cheng, L., Khalitov, R., Yu, T., Zhang, J., & Yang, Z. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.10.054 · Cited by 47 · see citing works
Liu, W., Guo, C., & Gao, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121612 · Cited by 40 · see citing works
A novel multi-scale convolutional neural network for motor imagery classification
Riyad, M., Khalil, M., & Adib, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102747 · Cited by 39 · see citing works
MI-DAGSC: A domain adaptation approach incorporating comprehensive information from MI-EEG signals
Zhang, D., Li, H., Xie, J., & Li, D. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.08.008 · Cited by 37 · see citing works
Cai, Z., Luo, T.-J., & Cao, X. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106156 · Cited by 36 · see citing works
Learning From Heterogeneous Eeg Signals with Differentiable Channel Reordering
Saeed, A., Grangier, D., Pietquin, O., & Zeghidour, N. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413712 · Cited by 33 · see citing works
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG SignalsPreprint
Rommel, C., Moreau, T., Paillard, J., & Gramfort, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.13695 · Cited by 28 · see citing works
Transformed common spatial pattern for motor imagery-based brain-computer interfaces
Ma, Z., Wang, K., Xu, M., Yi, W., Xu, F., & Ming, D. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1116721 · Cited by 23 · see citing works
Ai, Q., Liu, Y., Liu, Q., Ma, L., & Chen, K. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107526 · Cited by 12 · see citing works
Self-citationGuo, S., Wang, Y., Zhang, X., & Tang, B. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107122 · Cited by 8 · see citing works
Self-citationYassin, F. M., Norwawi, N. M., Noh, N. A., Alias, A., & Tamam, S. (2024) · Jordanian Journal of Computers and Information Technology · doi:10.5455/jjcit.71-1700410729 · Cited by 2 · see citing works
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
Bashivan, P., Rish, I., Yeasin, M., & Codella, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06448
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A comprehensive review of deep learning for motor imagery EEG: From healthy subjects to patients
Jiang, B., Wang, X., He, D., Cheng, S., Liao, M., Miao, D., Xia, Q., Yun, Z., & Li, G. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109680 · Cited by 3 · see citing works
Ren, Z., Du, X., Lin, Y., Tian, D., Ma, Y., & Wu, X. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109696 · Cited by 3 · see citing works
Zheng, M., Qian, Z., & Zhao, T. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1689647 · Cited by 1 · see citing works
Nan, J., Xu, P., Li, D., Zhu, F., Yang, C., & Zhang, H. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133463
Rahmani-Sane, G., & Haghani, S. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461454
Nan, J., Jin, X., Lin, J., Conghui, L., Li, D., & Zheng, Q. (2026) · Information · doi:10.3390/info17060592
Wahid, A. M., Baihaqi, W. M., & Nambo, H. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102484
- Authors
- Guo, S., Wang, Y., Liu, Y., Zhang, X., & Tang, B.
- Affiliations
- Chongqing University (CN)
- Wuhan University of Technology (CN)
- Corresponding author
- Wang, Yi
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2025-08
- Volume
- 106
- Pages
- 107756
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W4408253414
- Cited by
- 7
- References
- 58
- Citations by year
- 2026: 6 · 2025: 1
- FWCI
- 4.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Biomedical Signal Processing and Control (publishedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (2024CDJXY-017)
- Fundamental Research Funds for Central Universities of the Central South University
- National Natural Science Foundation of China (52175076)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- motor imagery classification, EEG decoding, cross-session generalization, data augmentation, domain adaptation, pseudo-labeling, lightweight neural networks, brain-computer interface
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