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Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders.
Adam: A method for stochastic optimization,
D. P. Kingma, J. Ba, · 2014
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The temple university hospital eeg data corpus,
I. Obeid, J. Picone, · 2016
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Sgdr: Stochastic gradient descent with warm restarts,
I. Loshchilov, F. Hutter, · 2016
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Deep learning with convolutional neural networks for eeg decoding and visualization,
R. T. Schirrmeister, J. T. Springenberg, L. D. J. Fiederer, M. Glasstetter, K. Eggensperger, M. Tangermann, F. Hutter, W. Burgard, T. Ball, · 2017
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Neural architecture search: A survey,
T. Elsken, J. H. Metzen, F. Hutter, · 2017
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Decoupled weight decay regularization,
I. Loshchilov, F. Hutter, · 2017
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Domain adaptation techniques for eeg-based emotion recognition: A comparative study on two public datasets,
Z. Lan, O. Sourina, L. Wang, R. Scherer, G. R. Müller-Putz, · 2018
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Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces,
V. J. Lawhern, A. J. Solon, N. R. Waytowich, S. M. Gordon, C. P. Hung, B. J. Lance, · 2018
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Transfer learning for time series classification,
H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, P.-A. Muller, · 2018
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Universal language model fine-tuning for text classification,
J. Howard, S. Ruder, · 2018
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Deep learning-based electroencephalography analysis: a systematic review,
Y. Roy, H. Banville, I. Albuquerque, A. Gramfort, T. H. Falk, J. Faubert, · 2019
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Similarity of neural network representations revisited,
S. Kornblith, M. Norouzi, H. Lee, G. Hinton, · 2019
Cited alongside, same era.
In search of lost domain generalization,
I. Gulrajani, D. Lopez-Paz, · 2020
Cited alongside, same era.
Machine-learning-based diagnostics of eeg pathology,
L. A. Gemein, R. T. Schirrmeister, P. Chrabąszcz, D. Wilson, J. Boedecker, A. Schulze-Bonhage, F. Hutter, T. Ball, · 2020
Cited alongside, same era.
Different scaling of linear models and deep learning in ukbiobank brain images versus machine-learning datasets,
M.-A. Schulz, B. T. Yeo, J. T. Vogelstein, J. Mourao-Miranada, J. N. Kather, K. Kording, B. Richards, D. Bzdok, · 2020
Cited alongside, same era.
A survey on data-efficient algorithms in big data era,
A. Adadi, · 2021
Cited alongside, same era.
Data augmentation for deep neural networks model in eeg classification task: a review,
Plex: Towards reliability using pretrained large model extensions,
D. Tran, J. Liu, M. W. Dusenberry, D. Phan, M. Collier, J. Ren, K. Han, Z. Wang, Z. Mariet, H. Hu, et al., · 2022
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Data augmentation for learning predictive models on eeg: a systematic comparison,
C. Rommel, J. Paillard, T. Moreau, A. Gramfort, · 2022
Later among the works it cites.
Understanding robust learning through the lens of representation similarities,
C. Cianfarani, A. N. Bhagoji, V. Sehwag, B. Zhao, H. Zheng, P. Mittal, · 2022
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From youtube to the brain: Transfer learning can improve brain-imaging predictions with deep learning,
N. Malik, D. Bzdok, · 2022
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Broken neural scaling laws,
E. Caballero, K. Gupta, I. Rish, D. Krueger, · 2022
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C. He, J. Liu, Y. Zhu, W. Du, · 2021
Cited alongside, same era.
A review on transfer learning in eeg signal analysis,
Z. Wan, R. Yang, M. Huang, N. Zeng, X. Liu, · 2021
Cited alongside, same era.
The nmt scalp eeg dataset: an open-source annotated dataset of healthy and pathological eeg recordings for predictive modeling,
H. A. Khan, R. Ul Ain, A. M. Kamboh, H. T. Butt, S. Shafait, W. Alamgir, D. Stricker, F. Shafait, · 2022
Cited alongside, same era.
Robust learning from corrupted eeg with dynamic spatial filtering,
H. Banville, S. U. Wood, C. Aimone, D.-A. Engemann, A. Gramfort, · 2022
Cited alongside, same era.
A survey on negative transfer,
W. Zhang, L. Deng, L. Zhang, D. Wu, · 2022
Cited alongside, same era.
Automatic detection of abnormal eeg signals using wavenet and lstm,
H. Albaqami, G. M. Hassan, A. Datta, · 2023
Closest in time.
Automated eeg pathology detection based on significant feature extraction and selection,
Y. Zhong, H. Wei, L. Chen, T. Wu, · 2023
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Cross-domain transfer of eeg to eeg or ecg learning for cnn classification models,
C.-Y. Yang, P.-C. Chen, W.-C. Huang, · 2023
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An extended clinical eeg dataset with 15,300 automatically labelled recordings for pathology decoding,
A.-K. Kiessner, R. T. Schirrmeister, L. A. Gemein, J. Boedecker, T. Ball, · 2023
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Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data,
P. Thölke, Y.-J. Mantilla-Ramos, H. Abdelhedi, C. Maschke, A. Dehgan, Y. Harel, A. Kemtur, L. M. Berrada, M. Sahraoui, T. Young, et al., · 2023
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