Fetching the paper…
Reading the bibliography…
Providing a promising pathway to link the human brain with external devices, Brain-Computer Interfaces (BCIs) have seen notable advancements in decoding capabilities, primarily driven by increasingly sophisticated techniques, especially deep learning.
Brunner, C., Leeb, R., Müller-Putz, G., Schlögl, A. & Pfurtscheller, G. BCI Competition 2008–Graz data set A. Institute For Knowledge Discovery (Laboratory Of Brain-Computer Interfaces), Graz University Of Technology
2008
Earlier work this paper cites.
Leeb, R., Brunner, C., Müller-Putz, G., Schlögl, A. & Pfurtscheller, G. BCI Competition 2008–Graz data set B. Graz University Of Technology, Austria
2008
Earlier work this paper cites.
Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. International Conference On Machine Learning
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. Proceedings Of The IEEE Conference On Computer Vision And Pattern Recognition
2016
Earlier work this paper cites.
Zanini, P., Congedo, M., Jutten, C., Said, S. & Berthoumieu, Y. Transfer learning: A Riemannian geometry framework with applications to brain–computer interfaces. IEEE Transactions On Biomedical Engineering
2017
Earlier work this paper cites.
Schirrmeister, R. et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping
2017
Earlier work this paper cites.
Lawhern, V. et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Journal Of Neural Engineering
2018
Earlier work this paper cites.
Roy, Y. et al. Deep learning-based electroencephalography analysis: a systematic review. Journal Of Neural Engineering
2019
Earlier work this paper cites.
Craik, A., He, Y. & Contreras-Vidal, J. Deep learning for electroencephalogram (EEG) classification tasks: a review. Journal Of Neural Engineering
2019
Earlier work this paper cites.
He, H. & Wu, D. Transfer learning for brain–computer interfaces: A Euclidean space data alignment approach. IEEE Transactions On Biomedical Engineering
2019
Earlier work this paper cites.
Wu, D., Xu, Y. & Lu, B. Transfer learning for EEG-based brain–computer interfaces: A review of progress made since 2016. IEEE Transactions On Cognitive And Developmental Systems
2020
Cited alongside, same era.
Kostas, D. & Rudzicz, F. Thinker invariance: enabling deep neural networks for BCI across more people. Journal Of Neural Engineering
2020
Cited alongside, same era.
Schneider, S. et al. Improving robustness against common corruptions by covariate shift adaptation. Advances In Neural Information Processing Systems
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Liang, J., Hu, D. & Feng, J. Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. International Conference On Machine Learning
2022
Later among the works it cites.
Xia, K., Deng, L., Duch, W. & Wu, D. Privacy-preserving domain adaptation for motor imagery-based brain-computer interfaces. IEEE Transactions On Biomedical Engineering
2022
Later among the works it cites.
2023
Closest in time.
Döbler, M., Marsden, R. & Yang, B. Robust mean teacher for continual and gradual test-time adaptation. Proceedings Of The IEEE/CVF Conference On Computer Vision And Pattern Recognition
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
An, S., Kim, S., Chikontwe, P. & Park, S. Few-shot relation learning with attention for EEG-based motor imagery classification. 2020 IEEE/RSJ International Conference On Intelligent Robots And Systems (IROS)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Ko, W., Jeon, E., Jeong, S., Phyo, J. & Suk, H. A survey on deep learning-based short/zero-calibration approaches for EEG-based brain–computer interfaces. Frontiers In Human Neuroscience
2021
Cited alongside, same era.
Xu, L. et al. Enhancing transfer performance across datasets for brain-computer interfaces using a combination of alignment strategies and adaptive batch normalization. Journal Of Neural Engineering
2021
Cited alongside, same era.
Rommel, C., Paillard, J., Moreau, T. & Gramfort, A. Data augmentation for learning predictive models on EEG: a systematic comparison. Journal Of Neural Engineering
2022
Cited alongside, same era.
Wang, Q., Fink, O., Van Gool, L. & Dai, D. Continual test-time domain adaptation. Proceedings Of The IEEE/CVF Conference On Computer Vision And Pattern Recognition
2022
Cited alongside, same era.
2023
Closest in time.
Yuan, L., Xie, B. & Li, S. Robust test-time adaptation in dynamic scenarios. Proceedings Of The IEEE/CVF Conference On Computer Vision And Pattern Recognition
2023
Closest in time.
2023
Closest in time.
Wang, K. et al. Privacy-Preserving Domain Adaptation for Intracranial EEG Classification via Information Maximization and Gaussian Mixture Model. IEEE Sensors Journal
2023
Closest in time.
Mao, T., Li, C., Zhao, Y., Song, R. & Chen, X. Online Test-Time Adaptation for Patient-Independent Seizure Prediction. IEEE Sensors Journal
2023
Closest in time.